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Deep Iterative Residual Convolutional Network for Single Image Super-Resolution

Rao Muhammad Umer, Gian Luca Foresti, Christian Micheloni

Auto-TLDR; ISRResCNet: Deep Iterative Super-Resolution Residual Convolutional Network for Single Image Super-resolution

Abstract Slides

Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. Most recent deep learning based SISR methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs. These existing SR methods do not take into account the image observation (physical) model and thus require a large number of network's trainable parameters with a huge volume of training data. To address these issues, we propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) that exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Extensive experimental results on various super-resolution benchmarks demonstrate that our method with a few trainable parameters improves results for different scaling factors in comparison with the state-of-art methods.

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Hierarchically Aggregated Residual Transformation for Single Image Super Resolution

Zejiang Hou, Sy Kung

Auto-TLDR; HARTnet: Hierarchically Aggregated Residual Transformation for Multi-Scale Super-resolution

Abstract Slides Poster Similar

Visual patterns usually appear at different scales/sizes in natural images. Multi-scale feature representation is of great importance for the single-image super-resolution(SISR) task to reconstruct image objects at different scales.However, such characteristic has been rarely considered by CNN-based SISR methods. In this work, we propose a novel build-ing block, i.e. hierarchically aggregated residual transformation(HART), to achieve multi-scale feature representation in each layer of the network. Within each HART block, we connect multiple convolutions in a hierarchical residual-like manner, which greatly expands the range of effective receptive fields and helps to detect image features at different scales. To theoretically understand the proposed HART block, we recast SISR as an optimal control problem and show that HART effectively approximates the classical4th-order Runge-Kutta method, which has the merit of small local truncation error for solving numerical ordinary differential equation. By cascading the proposed HART blocks, we establish our high-performing HARTnet. Comparedwith existing SR state-of-the-arts (including those in NTIRE2019 SR Challenge leaderboard), the proposed HARTnet demonstrates consistent PSNR/SSIM performance improvements on various benchmark datasets under different degradation models.Moreover, HARTnet can efficiently restore more faithful high-resolution images than comparative SR methods (cf. Figure 1).

LiNet: A Lightweight Network for Image Super Resolution

Armin Mehri, Parichehr Behjati Ardakani, Angel D. Sappa

Auto-TLDR; LiNet: A Compact Dense Network for Lightweight Super Resolution

Abstract Slides Poster Similar

This paper proposes a new lightweight network, LiNet, that enhancing technical efficiency in lightweight super resolution and operating approximately like very large and costly networks in terms of number of network parameters and operations. The proposed architecture allows the network to learn more abstract properties by avoiding low-level information via multiple links. LiNet introduces a Compact Dense Module, which contains set of inner and outer blocks, to efficiently extract meaningful information, to better leverage multi-level representations before upsampling stage, and to allow an efficient information and gradient flow within the network. Experiments on benchmark datasets show that the proposed LiNet achieves favorable performance against lightweight state-of-the-art methods.

Progressive Splitting and Upscaling Structure for Super-Resolution

Qiang Li, Tao Dai, Shutao Xia

Auto-TLDR; PSUS: Progressive and Upscaling Layer for Single Image Super-Resolution

Abstract Slides Poster Similar

Recently, very deep convolutional neural networks (CNNs) have shown great success in single image super-resolution (SISR). Most of these methods focus on the design of network architecture and adopt a sub-pixel convolution layer at the end of network, but few have paid attention to exploring potential representation ability of upscaling layer. Sub-pixel convolution layer aggregates several low resolution (LR) feature maps and builds super-resolution (SR) images in a single step. However, those LR feature maps share similar patterns as they are extracted from a single trunk network. We believe that the mapping relationships between input image and each LR feature map are not consistent. Inspired by this, we propose a novel progressive splitting and upscaling structure, termed PSUS, which generates decoupled feature maps for upscaling layer to get better SR image. Experiments show that our method can not only speed up the convergence, but also achieve considerable improvement on image quality with fewer parameters and lower computational complexity.

Single Image Super-Resolution with Dynamic Residual Connection

Karam Park, Jae Woong Soh, Nam Ik Cho

Auto-TLDR; Dynamic Residual Attention Network for Lightweight Single Image Super-Residual Networks

Abstract Slides Poster Similar

Deep convolutional neural networks have shown significant improvement in the single image super-resolution (SISR) field. Recently, there have been attempts to solve the SISR problem using lightweight networks, considering limited computational resources for real-world applications. Especially for lightweight networks, balancing between parameter demand and performance is very difficult to adjust, and most lightweight SISR networks are manually designed based on a huge number of brute-force experiments. Besides, a critical key to the network performance relies on the skip connection of building blocks that are repeatedly in the architecture. Notably, in previous works, these connections are pre-defined and manually determined by human researchers. Hence, they are less flexible to the input image statistics, and there can be a better solution for the given number of parameters. Therefore, we focus on the automated design of networks regarding the connection of basic building blocks (residual networks), and as a result, propose a dynamic residual attention network (DRAN). The proposed method allows the network to dynamically select residual paths depending on the input image, based on the idea of attention mechanism. For this, we design a dynamic residual module that determines the residual paths between the basic building blocks for the given input image. By finding optimal residual paths between the blocks, the network can selectively bypass informative features needed to reconstruct the target high-resolution (HR) image. Experimental results show that our proposed DRAN outperforms most of the existing state-of-the-arts lightweight models in SISR.

MBD-GAN: Model-Based Image Deblurring with a Generative Adversarial Network

Li Song, Edmund Y. Lam

Auto-TLDR; Model-Based Deblurring GAN for Inverse Imaging

Abstract Slides Poster Similar

This paper presents a methodology to tackle inverse imaging problems by leveraging the synergistic power of imaging model and deep learning. The premise is that while learning-based techniques have quickly become the methods of choice in various applications, they often ignore the prior knowledge embedded in imaging models. Incorporating the latter has the potential to improve the image estimation. Specifically, we first provide a mathematical basis of using generative adversarial network (GAN) in inverse imaging through considering an optimization framework. Then, we develop the specific architecture that connects the generator and discriminator networks with the imaging model. While this technique can be applied to a variety of problems, from image reconstruction to super-resolution, we take image deblurring as the example here, where we show in detail the implementation and experimental results of what we call the model-based deblurring GAN (MBD-GAN).

Residual Fractal Network for Single Image Super Resolution by Widening and Deepening

Jiahang Gu, Zhaowei Qu, Xiaoru Wang, Jiawang Dan, Junwei Sun

Auto-TLDR; Residual fractal convolutional network for single image super-resolution

Abstract Slides Poster Similar

The architecture of the convolutional neural network (CNN) plays an important role in single image super-resolution (SISR). However, most models proposed in recent years usually transplant methods or architectures that perform well in other vision fields. Thence they do not combine the characteristics of super-resolution (SR) and ignore the key information brought by the recurring texture feature in the image. To utilize patch-recurrence in SR and the high correlation of texture, we propose a residual fractal convolutional block (RFCB) and expand its depth and width to obtain residual fractal network (RFN), which contains deep residual fractal network (DRFN) and wide residual fractal network (WRFN). RFCB is recursive with multiple branches of magnified receptive field. Through the phased feature fusion module, the network focuses on extracting high-frequency texture feature that repeatedly appear in the image. We also introduce residual in residual (RIR) structure to RFCB that enables abundant low-frequency feature feed into deeper layers and reduce the difficulties of network training. RFN is the first supervised learning method to combine the patch-recurrence characteristic in SISR into network design. Extensive experiments demonstrate that RFN outperforms state-of-the-art SISR methods in terms of both quantitative metrics and visual quality, while the amount of parameters has been greatly optimized.

Deep Universal Blind Image Denoising

Jae Woong Soh, Nam Ik Cho

Auto-TLDR; Image Denoising with Deep Convolutional Neural Networks

Abstract Slides Similar

Image denoising is an essential part of many image processing and computer vision tasks due to inevitable noise corruption during image acquisition. Traditionally, many researchers have investigated image priors for the denoising, within the Bayesian perspective based on image properties and statistics. Recently, deep convolutional neural networks (CNNs) have shown great success in image denoising by incorporating large-scale synthetic datasets. However, they both have pros and cons. While the deep CNNs are powerful for removing the noise with known statistics, they tend to lack flexibility and practicality for the blind and real-world noise. Moreover, they cannot easily employ explicit priors. On the other hand, traditional non-learning methods can involve explicit image priors, but they require considerable computation time and cannot exploit large-scale external datasets. In this paper, we present a CNN-based method that leverages the advantages of both methods based on the Bayesian perspective. Concretely, we divide the blind image denoising problem into sub-problems and conquer each inference problem separately. As the CNN is a powerful tool for inference, our method is rooted in CNNs and propose a novel design of network for efficient inference. With our proposed method, we can successfully remove blind and real-world noise, with a moderate number of parameters of universal CNN.

Cross-Layer Information Refining Network for Single Image Super-Resolution

Hongyi Zhang, Wen Lu, Xiaopeng Sun

Auto-TLDR; Interlaced Spatial Attention Block for Single Image Super-Resolution

Abstract Slides Poster Similar

Recently, deep learning-based image super-resolution (SR) has made a remarkable progress. However, previous SR methods rarely focus on the correlation between adjacent layers, which leads to underutilization of the information extracted by each convolutional layer. To address these problem, we design a simple and efficient cross-layer information refining network (CIRN) for single image super-resolution. Concretely, we propose the interlaced spatial attention block (ISAB) to measure the correlation between the adjacent layers feature maps and adaptively rescale spatial-wise features for refining the information. Owing to the two stage information propagation strategy, the CIRN can distill the primary information of adjacent layers without introducing too many parameters. Extensive experiments on benchmark datasets illustrate that our method achieves better accuracy than state-of-the-art methods even in 16× scale, spcifically it has a better banlance between performance and parameters.

Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution

Xiaoyu Xiang, Qian Lin, Jan Allebach

Auto-TLDR; A Context-Aware Joint CAR and SR Neural Network for High-Resolution Text Recognition and Face Detection

Abstract Slides Poster Similar

Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR and SR in one-stage. Finally, a deep reconstruction network is adopted to predict high quality and high-resolution images. Evaluation on CAR and SR benchmark datasets shows that our CAJNN model outperforms previous methods and also takes 26.2% less runtime. Based on this model, we explore addressing two critical challenges in high-level computer vision: optical character recognition of low-resolution texts, and extremely tiny face detection. We demonstrate that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition (from 85.30% to 85.75%) and the average precision for tiny face detection (from 0.317 to 0.611).

RSAN: Residual Subtraction and Attention Network for Single Image Super-Resolution

Shuo Wei, Xin Sun, Haoran Zhao, Junyu Dong

Auto-TLDR; RSAN: Residual subtraction and attention network for super-resolution

Abstract Slides Similar

The single-image super-resolution (SISR) aims to recover a potential high-resolution image from its low-resolution version. Recently, deep learning-based methods have played a significant role in super-resolution field due to its effectiveness and efficiency. However, most of the SISR methods neglect the importance among the feature map channels. Moreover, they can not eliminate the redundant noises, making the output image be blurred. In this paper, we propose the residual subtraction and attention network (RSAN) for powerful feature expression and channels importance learning. More specifically, RSAN firstly implements one redundance removal module to learn noise information in the feature map and subtract noise through residual learning. Then it introduces the channel attention module to amplify high-frequency information and suppress the weight of effectless channels. Experimental results on extensive public benchmarks demonstrate our RSAN achieves significant improvement over the previous SISR methods in terms of both quantitative metrics and visual quality.

Wavelet Attention Embedding Networks for Video Super-Resolution

Young-Ju Choi, Young-Woon Lee, Byung-Gyu Kim

Auto-TLDR; Wavelet Attention Embedding Network for Video Super-Resolution

Abstract Slides Poster Similar

Recently, Video super-resolution (VSR) has become more crucial as the resolution of display has been grown. The majority of deep learning-based VSR methods combine the convolutional neural networks (CNN) with motion compensation or alignment module to estimate high-resolution (HR) frame from low-resolution (LR) frames. However, most of previous methods deal with the spatial features equally and may result in the misaligned temporal features by pixel-based motion compensation and alignment module. It can lead to the damaging effect on the accuracy of the estimated HR feature. In this paper, we propose a wavelet attention embedding network (WAEN), including wavelet embedding network (WENet) and attention embedding network (AENet), to fully exploit the spatio-temporal informative features. The WENet is operated as a spatial feature extractor of individual low and high-frequency information based on 2-D Haar discrete wavelet transform. The meaningful temporal feature is extracted in the AENet through utilizing the weighted attention map between frames. Experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods.

On-Device Text Image Super Resolution

Dhruval Jain, Arun Prabhu, Gopi Ramena, Manoj Goyal, Debi Mohanty, Naresh Purre, Sukumar Moharana

Auto-TLDR; A Novel Deep Neural Network for Super-Resolution on Low Resolution Text Images

Abstract Slides Poster Similar

Recent research on super-resolution (SR) has wit- nessed major developments with the advancements of deep convolutional neural networks. There is a need for information extraction from scenic text images or even document images on device, most of which are low-resolution (LR) images. Therefore, SR becomes an essential pre-processing step as Bicubic Upsampling, which is conventionally present in smartphones, performs poorly on LR images. To give the user more control over his privacy, and to reduce the carbon footprint by reducing the overhead of cloud computing and hours of GPU usage, executing SR models on the edge is a necessity in the recent times. There are various challenges in running and optimizing a model on resource-constrained platforms like smartphones. In this paper, we present a novel deep neural network that reconstructs sharper character edges and thus boosts OCR confidence. The proposed architecture not only achieves significant improvement in PSNR over bicubic upsampling on various benchmark datasets but also runs with an average inference time of 11.7 ms per image. We have outperformed state-of-the-art on the Text330 dataset. We also achieve an OCR accuracy of 75.89% on the ICDAR 2015 TextSR dataset, where ground truth has an accuracy of 78.10%.

Improving Low-Resolution Image Classification by Super-Resolution with Enhancing High-Frequency Content

Liguo Zhou, Guang Chen, Mingyue Feng, Alois Knoll

Auto-TLDR; Super-resolution for Low-Resolution Image Classification

Abstract Slides Poster Similar

With the prosperous development of Convolutional Neural Networks, currently they can perform excellently on visual understanding tasks when the input images are high quality and common quality images. However, large degradation in performance always occur when the input images are low quality images. In this paper, we propose a new super-resolution method in order to improve the classification performance for low-resolution images. In an image, the regions in which pixel values vary dramatically contain more abundant high frequency contents compared to other parts. Based on this fact, we design a weight map and integrate it with a super-resolution CNN training framework. During the process of training, this weight map can find out positions of the high frequency pixels in ground truth high-resolution images. After that, the pixel-level loss function takes effect only at these found positions to minimize the difference between reconstructed high-resolution images and ground truth high-resolution images. Compared with other state-of-the-art super-resolution methods, the experiment results show that our method can recover more high-frequency contents in high-resolution image reconstructing, and better improve the classification accuracy after low-resolution image preprocessing.

Efficient Super Resolution by Recursive Aggregation

Zhengxiong Luo Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan

Auto-TLDR; Recursive Aggregation Network for Efficient Deep Super Resolution

Abstract Slides Poster Similar

Deep neural networks have achieved remarkable results on image super resolution (SR), but the efficiency problem of deep SR networks is rarely studied. We experimentally find that many sequentially stacked convolutional blocks in nowadays SR networks are far from being fully optimized, which largely damages their overall efficiency. It indicates that comparable or even better results could be achieved with less but sufficiently optimized blocks. In this paper, we try to construct more efficient SR model via the proposed recursive aggregation network (RAN). It recursively aggregates convolutional blocks in different orders, and avoids too many sequentially stacked blocks. In this way, multiple shortcuts are introduced in RAN, and help gradients easier flow to all inner layers, even for very deep SR networks. As a result, all blocks in RAN can be better optimized, thus RAN can achieve better performance with smaller model size than existing methods.

Detail-Revealing Deep Low-Dose CT Reconstruction

Xinchen Ye, Yuyao Xu, Rui Xu, Shoji Kido, Noriyuki Tomiyama

Auto-TLDR; A Dual-branch Aggregation Network for Low-Dose CT Reconstruction

Abstract Slides Poster Similar

Low-dose CT imaging emerges with low radiation risk due to the reduction of radiation dose, but brings negative impact on the imaging quality. This paper addresses the problem of low-dose CT reconstruction. Previous methods are unsatisfactory due to the inaccurate recovery of image details under the strong noise generated by the reduction of radiation dose, which directly affects the final diagnosis. To suppress the noise effectively while retain the structures well, we propose a detail-revealing dual-branch aggregation network to effectively reconstruct the degraded CT image. Specifically, the main reconstruction branch iteratively exploits and compensates the reconstruction errors to gradually refine the CT image, while the prior branch is to learn the structure details as prior knowledge to help recover the CT image. A sophisticated detail-revealing loss is designed to fuse the information from both branches and guide the learning to obtain better performance from pixel-wise and holistic perspectives respectively. Experimental results show that our method outperforms the state-of-art methods in both PSNR and SSIM metrics.

Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information

Shuang Liu, Chengyi Xiong, Zhirong Gao

Auto-TLDR; Learning-based Face Super-Resolution with Incremental Boosting Facial Parsing Information

Abstract Slides Poster Similar

Recently, facial priors based face super-resolution (SR) methods have obtained significant performance gains in dealing with extremely degraded facial images, and facial priors have also been proved useful in facilitating the inference of face images. Based on this, how to fully fuse facial priors into deep features to improve face SR performance has attracted a major attention. In this paper, we propose a learning-based face SR approach with incremental boosting facial parsing information (IFPSR) for high-magnification of low-resolution faces. The proposed IFPSR method consists of three main parts: i) a three-stage parsing map embedded features upsampling network, in which image recovery and prior estimation processes are performed simultaneously and progressively to improve the image resolution; ii) a progressive training method and a joint facial attention and heatmap loss to obtain better facial attributes; iii) the channel attention strategy in residual dense blocks to adaptively learn facial features. Extensive experimental results show that compared with the state-of-the-art methods in terms of quantitative and qualitative metrics, our approach can achieve an outstanding balance between SR image quality and low network complexity.

DID: A Nested Dense in Dense Structure with Variable Local Dense Blocks for Super-Resolution Image Reconstruction

Longxi Li, Hesen Feng, Bing Zheng, Lihong Ma, Jing Tian

Auto-TLDR; DID: Deep Super-Residual Dense Network for Image Super-resolution Reconstruction

Abstract Slides Poster Similar

The success of single image super-resolution reconstruction (SR) relies on a refined mapping from low-resolution (LR) examples to high-resolution (HR) signals. However, the relation is sometimes chaos, especially in a deep SR network. We try to improve the mapping interpretability in two folds: i) The variable local dense blocks (VLDB) are suggested to match receptive fields in different depths of a residual dense network (RDN), with each VLDB a dyadic increment of layer numbers than its predecessor. ii) Based on VLDBs, a dense in dense (DID) network is created. It substitutes nodes in a regular RDN with super nodes, i.e. VLDBs; and formulates a joint learning by flexible hierarchical feature scaling, reusing and long-short term aggregating. VLDBs deal with feature underfitting occurred when a big receptive field meets a fixed-depth dense block, and the DID network provides a relative complete feature dictionary to preserve details for feature shift, dilating and grouping in high dimension image reconstruction. To demonstrate the validness of DID structure, detail experiments are performed on the benchmark datasets Set5, Set14, B100 and Urban100, where the accuracy PSNR and the visual perceptive SSIM are superior to most state-of-the-art methods. Besides, due to the depth adaption of VLDBs and its nesting in generalized RDN，DID network is converged easily and gradient explosion or disappearance are alleviated even when network deepens.

D3Net: Joint Demosaicking, Deblurring and Deringing

Tomas Kerepecky, Filip Sroubek

Auto-TLDR; Joint demosaicking deblurring and deringing network with light-weight architecture inspired by the alternating direction method of multipliers

Abstract Slides Similar

Images acquired with standard digital cameras have Bayer patterns and suffer from lens blur. A demosaicking step is implemented in every digital camera, yet blur often remains unattended due to computational cost and instability of deblurring algorithms. Linear methods, which are computationally less demanding, produce ringing artifacts in deblurred images. Complex non-linear deblurring methods avoid artifacts, however their complexity imply offline application after camera demosaicking, which leads to sub-optimal performance. In this work, we propose a joint demosaicking deblurring and deringing network with a light-weight architecture inspired by the alternating direction method of multipliers. The proposed network has a transparent and clear interpretation compared to other black-box data driven approaches. We experimentally validate its superiority over state-of-the-art demosaicking methods with offline deblurring.

Neural Architecture Search for Image Super-Resolution Using Densely Connected Search Space: DeCoNAS

Joon Young Ahn, Nam Ik Cho

Auto-TLDR; DeCoNASNet: Automated Neural Architecture Search for Super-Resolution

Abstract Slides Poster Similar

Abstract—The recent progress of deep convolutional neural networks has enabled great success in single image superresolution (SISR) and many other vision tasks. Their performances are also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this paper, we expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. We use a hierarchical search strategy to find the best connection with local and global features. In this process, we define a complexitybased penalty for solving image super-resolution, which can be considered a multi-objective problem. Experiments show that our DeCoNASNet outperforms the state-of-the-art lightweight superresolution networks designed by handcraft methods and existing NAS-based design.

Deep Residual Attention Network for Hyperspectral Image Reconstruction

Kohei Yorimoto, Xian-Hua Han

Auto-TLDR; Deep Convolutional Neural Network for Hyperspectral Image Reconstruction from a Snapshot

Abstract Slides Poster Similar

Coded aperture snapshot spectral imaging (CASSI) captures a full frame spectral image as a single compressive image and is mandatory to reconstruct the underlying hyperspectral image (HSI) from the snapshot as the post-processing, which is challenge inverse problem due to its ill-posed nature. Existing methods for HSI reconstruction from a snapshot usually employs optimization for solving the formulated image degradation model regularized with the empirically designed priors, and still cannot achieve enough reconstruction accuracy for real HS image analysis systems. Motivated by the recent advances of deep learning for different inverse problems, deep learning based HSI reconstruction method has attracted a lot of attention, and can boost the reconstruction performance. This study proposes a novel deep convolutional neural network (DCNN) based framework for effectively learning the spatial structure and spectral attribute in the underlying HSI with the reciprocal spatial and spectral modules. Further, to adaptively leverage the useful learned feature for better HSI image reconstruction, we integrate residual attention modules into our DCNN via exploring both spatial and spectral attention maps. Experimental results on two benchmark HSI datasets show that our method outperforms state-of-the-art methods in both quantitative values and visual effect.

TinyVIRAT: Low-Resolution Video Action Recognition

Ugur Demir, Yogesh Rawat, Mubarak Shah

Auto-TLDR; TinyVIRAT: A Progressive Generative Approach for Action Recognition in Videos

Abstract Slides Poster Similar

The existing research in action recognition is mostly focused on high-quality videos where the action is distinctly visible. In real-world surveillance environments, the actions in videos are captured at a wide range of resolutions. Most activities occur at a distance with a small resolution and recognizing such activities is a challenging problem. In this work, we focus on recognizing tiny actions in videos. We introduce a benchmark dataset, TinyVIRAT, which contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging. We propose a novel method for recognizing tiny actions in videos which utilizes a progressive generative approach to improve the quality of low-resolution actions. The proposed method also consists of a weakly trained attention mechanism which helps in focusing on the activity regions in the video. We perform extensive experiments to benchmark the proposed TinyVIRAT dataset and observe that the proposed method significantly improves the action recognition performance over baselines. We also evaluate the proposed approach on synthetically resized action recognition datasets and achieve state-of-the-art results when compared with existing methods. The dataset and code will be publicly available.

Multi-Laplacian GAN with Edge Enhancement for Face Super Resolution

Shanlei Ko, Bi-Ru Dai

Auto-TLDR; Face Image Super-Resolution with Enhanced Edge Information

Abstract Slides Poster Similar

Face image super-resolution has become a research hotspot in the field of image processing. Nowadays, more and more researches add additional information, such as landmark, identity, to reconstruct high resolution images from low resolution ones, and have a good performance in quantitative terms and perceptual quality. However, these additional information is hard to obtain in many cases. In this work, we focus on reconstructing face images by extracting useful information from face images directly rather than using additional information. By observing edge information in each scale of face images, we propose a method to reconstruct high resolution face images with enhanced edge information. In additional, with the proposed training procedure, our method reconstructs photo-realistic images in upscaling factor 8x and outperforms state-of-the-art methods both in quantitative terms and perceptual quality.

Quantifying Model Uncertainty in Inverse Problems Via Bayesian Deep Gradient Descent

Riccardo Barbano, Chen Zhang, Simon Arridge, Bangti Jin

Auto-TLDR; Bayesian Neural Networks for Inverse Reconstruction via Bayesian Knowledge-Aided Computation

Abstract Slides Poster Similar

Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e.g., deep neural networks. These techniques have demonstrated state-of-the-art performances for several imaging tasks, but they often do not provide uncertainty on the obtained reconstructions. In this work, we develop a novel scalable data-driven knowledge-aided computational framework to quantify the model uncertainty via Bayesian neural networks. The approach builds on and extends deep gradient descent, a recently developed greedy iterative training scheme, and recasts it within a probabilistic framework. Scalability is achieved by being hybrid in the architecture: only the last layer of each block is Bayesian, while the others remain deterministic, and by being greedy in training. The framework is showcased on one representative medical imaging modality, viz. computed tomography with either sparse view or limited view data, and exhibits competitive performance with respect to state-of-the-art benchmarks, e.g., total variation, deep gradient descent and learned primal-dual.

Tarsier: Evolving Noise Injection inSuper-Resolution GANs

Baptiste Roziere, Nathanaël Carraz Rakotonirina, Vlad Hosu, Rasoanaivo Andry, Hanhe Lin, Camille Couprie, Olivier Teytaud

Auto-TLDR; Evolutionary Super-Resolution using Diagonal CMA

Abstract Slides Poster Similar

Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by nESRGAN+,which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve nESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms nESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection.

Small Object Detection Leveraging on Simultaneous Super-Resolution

Hong Ji, Zhi Gao, Xiaodong Liu, Tiancan Mei

Auto-TLDR; Super-Resolution via Generative Adversarial Network for Small Object Detection

Abstract Poster Similar

Despite the impressive advancement achieved in object detection, the detection performance of small object is still far from satisfactory due to the lack of sufficient detailed appearance to distinguish it from similar objects. Inspired by the positive effects of super-resolution for object detection, we propose a general framework that can be incorporated with most available detector networks to significantly improve the performance of small object detection, in which the low-resolution image is super-resolved via generative adversarial network (GAN) in an unsupervised manner. In our method, the super-resolution network and the detection network are trained jointly and alternately with each other fixed. In particular, the detection loss is back-propagated into the super-resolution network during training to facilitate detection. Compared with available simultaneous super-resolution and detection methods which heavily rely on low-/high-resolution image pairs, our work breaks through such restriction via applying the CycleGAN strategy, achieving increased generality and applicability, while remaining an elegant structure. Extensive experiments on datasets from both computer vision and remote sensing communities demonstrate that our method works effectively on a wide range of complex scenarios, resulting in best performance that significantly outperforms many state-of-the-art approaches.

Automatical Enhancement and Denoising of Extremely Low-Light Images

Yuda Song, Yunfang Zhu, Xin Du

Auto-TLDR; INSNet: Illumination and Noise Separation Network for Low-Light Image Restoring

Abstract Slides Poster Similar

Deep convolutional neural networks (DCNN) based methodologies have achieved remarkable performance on various low-level vision tasks recently. Restoring images captured at night is one of the trickiest low-level vision tasks due to its high-level noise and low-level intensity. We propose a DCNN-based methodology, Illumination and Noise Separation Network (INSNet), which performs both denoising and enhancement on these extremely low-light images. INSNet fully utilizes global-ware features and local-ware features using the modified network structure and image sampling scheme. Compared to well-designed complex neural networks, our proposed methodology only needs to add a bypass network to the existing network. However, it can boost the quality of recovered images dramatically but only increase the computational cost by less than 0.1%. Even without any manual settings, INSNet can stably restore the extremely low-light images to desired high-quality images.

Edge-Guided CNN for Denoising Images from Portable Ultrasound Devices

Yingnan Ma, Fei Yang, Anup Basu

Auto-TLDR; Edge-Guided Convolutional Neural Network for Portable Ultrasound Images

Abstract Slides Poster Similar

Ultrasound is a non-invasive tool that is useful for medical diagnosis and treatment. To reduce long wait times and add convenience to patients, portable ultrasound scanning devices are becoming increasingly popular. These devices can be held in one palm, and are compatible with modern cell phones. However, the quality of ultrasound images captured from the portable scanners is relatively poor compared to standard ultrasound scanning systems in hospitals. To improve the quality of the ultrasound images obtained from portable ultrasound devices, we propose a new neural network architecture called Edge-Guided Convolutional Neural Network (EGCNN), which can preserve significant edge information in ultrasound images when removing noise. We also study and compare the effectiveness of classical filtering approaches in removing speckle noise in these images. Experimental results show that after applying the proposed EGCNN, various organs can be better recognized from ultrasound images. This approach is expected to lead to better accuracy in diagnostics in the future.

Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks

Shoma Iwai, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi

Auto-TLDR; GAN-based Image Compression at Low Bitrates

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We propose a GAN-based image compression method working at extremely low bitrates below 0.1bpp. Most existing learned image compression methods suffer from blur at extremely low bitrates. Although GAN can help to reconstruct sharp images, there are two drawbacks. First, GAN makes train- ing unstable. Second, the reconstructions often contain unpleasing noise or artifacts. To address both of the drawbacks, our method adopts two-stage training and network interpolation. The two- stage training is effective to stabilize the training. Moreover, the network interpolation utilizes the models in both stages and reduces undesirable noise and artifacts, while maintaining important edges. Hence, we can control the trade-off between perceptual quality and fidelity without re-training models. The experimental results show that our model can reconstruct high quality images. Furthermore, our user study confirms that our reconstructions are preferable to state-of-the-art GAN-based image compression model.

Single Image Deblurring Using Bi-Attention Network

Yaowei Li, Ye Luo, Jianwei Lu

Auto-TLDR; Bi-Attention Neural Network for Single Image Deblurring

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Recently, deep convolutional neural networks have been extensively applied into image deblurring and have achieved remarkable performance. However, most CNN-based image deblurring methods focus on simply increasing network depth, neglecting the contextual information of the blurred image and the reconstructed image. Meanwhile, most encoder-decoder based methods rarely exploit encoder's multi-layer features. To address these issues, we propose a bi-attention neural network for single image deblurring, which mainly consists of a bi-attention network and a feature fusion network. Specifically, two criss-cross attention modules are plugged before and after the encoder-decoder to capture long-range spatial contextual information in the blurred image and the reconstructed image simultaneously, and the feature fusion network combines multi-layer features from encoder to enable the decoder reconstruct the image with multi-scale features. The whole network is end-to-end trainable. Quantitative and qualitative experiment results validate that the proposed network outperforms state-of-the-art methods in terms of PSNR and SSIM on benchmark datasets.

Super-Resolution Guided Pore Detection for Fingerprint Recognition

Syeda Nyma Ferdous, Ali Dabouei, Jeremy Dawson, Nasser M. Nasarabadi

Auto-TLDR; Super-Resolution Generative Adversarial Network for Fingerprint Recognition Using Pore Features

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Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only if the fingerprint image is of high resolution which necessitates a model that enhances the image quality of the conventional 500 ppi legacy fingerprints preserving the fine details. To find a solution for recovering pore information from low-resolution fingerprints, we adopt a joint learning-based approach that combines both super-resolution and pore detection networks. Our modified single image Super-Resolution Generative Adversarial Network (SRGAN) framework helps to reliably reconstruct high-resolution fingerprint samples from low-resolution ones assisting the pore detection network to identify pores with a high accuracy. The network jointly learns a distinctive feature representation from a real low-resolution fingerprint sample and successfully synthesizes a high-resolution sample from it. To add discriminative information and uniqueness for all the subjects, we have integrated features extracted from a deep fingerprint verifier with the SRGAN quality discriminator. We also add ridge reconstruction loss, utilizing ridge patterns to make the best use of extracted features. Our proposed method solves the recognition problem by improving the quality of fingerprint images. High recognition accuracy of the synthesized samples that is close to the accuracy achieved using the original high-resolution images validate the effectiveness of our proposed model.

GAN-Based Image Deblurring Using DCT Discriminator

Hiroki Tomosada, Takahiro Kudo, Takanori Fujisawa, Masaaki Ikehara

Auto-TLDR; DeblurDCTGAN: A Discrete Cosine Transform for Image Deblurring

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In this paper, we propose high quality image debluring by using discrete cosine transform (DCT) with less computational complexity. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale architecture of CNN restores blurred image cleary and suppresses more ringing artifacts or block noise, but it takes much time to process. To solve these problems, we propose a method that preserves texture and suppresses ringing artifacts in the restored image without multi-scale architecture using DCT based loss named ``DeblurDCTGAN.''. It compares frequency domain of the images made from deblurred image and grand truth image by using DCT. Hereby, DeblurDCTGAN can reduce block noise or ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances on both PSNR and SSIM comparing with other conventional methods in both GoPro test Dataset and DVD test Dataset. Also, the running time per pair of DeblurDCTGAN is faster than others.

Ultrasound Image Restoration Using Weighted Nuclear Norm Minimization

Hanmei Yang, Ye Luo, Jianwei Lu, Jian Lu

Auto-TLDR; A Nonconvex Low-Rank Matrix Approximation Model for Ultrasound Images Restoration

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Ultrasound images are often contaminated by speckle noise during the acquisition process, which influences the performance of subsequent application. The paper introduces a nonconvex low-rank matrix approximation model for ultrasound images restoration, which integrates the weighted unclear norm minimization (WNNM) and data fidelity term. WNNM can adaptively assign weights on differnt singular values to preserve more details in restored images. The fidelity term about ultrasound images do not be utilized in existing low-rank ultrasound denoising methods. This optimization question can effectively solved by alternating direction method of multipliers (ADMM). The experimental results on simulated images and real medical ultrasound images demonstrate the excellent performance of the proposed method compared with other four state-of-the-art methods.

SIDGAN: Single Image Dehazing without Paired Supervision

Pan Wei, Xin Wang, Lei Wang, Ji Xiang, Zihan Wang

Auto-TLDR; DehazeGAN: An End-to-End Generative Adversarial Network for Image Dehazing

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Single image dehazing is challenging without scene airlight and transmission map. Most of existing dehazing algorithms tend to estimate key parameters based on manual designed priors or statistics, which may be invalid in some scenarios. Although deep learning-based dehazing methods provide an effective solution, most of them rely on paired training datasets, which are prohibitively difficult to be collected in real world. In this paper, we propose an effective end-to-end generative adversarial network for image dehazing, named DehazeGAN. The proposed DehazeGAN adopts a U-net architecture with a novel color-consistency loss derived from dark channel prior and perceptual loss, which can be trained in an unsupervised fashion without paired synthetic datasets. We create a RealHaze dataset for network training, including 4,000 outdoor hazy images and 4,000 haze-free images. Extensive experiments demonstrate that our proposed DehazeGAN achieves better performance than existing state-of-the-art methods on both synthetic datasets and real-world datasets in terms of PSNR, SSIM, and subjective visual experience.

A NoGAN Approach for Image and Video Restoration and Compression Artifact Removal

Mameli Filippo, Marco Bertini, Leonardo Galteri, Alberto Del Bimbo

Auto-TLDR; Deep Neural Network for Image and Video Compression Artifact Removal and Restoration

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Lossy image and video compression algorithms introduce several different types of visual artifacts that reduce the visual quality of the compressed media, and the higher the compression rate the higher is the strength of these artifacts. In this work, we describe an approach for visual quality improvement of compressed images and videos to be performed at presentation time, so to obtain the benefits of fast data transfer and reduced data storage, while enjoying a visual quality that could be obtained only reducing the compression rate. To obtain this result we propose to use a deep neural network trained using the NoGAN approach, adapting the popular DeOldify architecture used for colorization. We show how the proposed method can be applied both to image and video compression artifact removal and restoration.

Snapshot Hyperspectral Imaging Based on Weighted High-Order Singular Value Regularization

Hua Huang, Cheng Niankai, Lizhi Wang

Auto-TLDR; High-Order Tensor Optimization for Hyperspectral Imaging

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Snapshot hyperspectral imaging can capture the 3D hyperspectral image (HSI) with a single 2D measurement and has attracted increasing attention recently. Recovering the underlying HSI from the compressive measurement is an ill-posed problem and exploiting the image prior is essential for solving this ill-posed problem. However, existing reconstruction methods always start from modeling image prior with the 1D vector or 2D matrix and cannot fully exploit the structurally spectral-spatial nature in 3D HSI, thus leading to a poor fidelity. In this paper, we propose an effective high-order tensor optimization based method to boost the reconstruction fidelity for snapshot hyperspectral imaging. We first build high-order tensors by exploiting the spatial-spectral correlation in HSI. Then, we propose a weight high-order singular value regularization (WHOSVR) based low-rank tensor recovery model to characterize the structure prior of HSI. By integrating the structure prior in WHOSVR with the system imaging process, we develop an optimization framework for HSI reconstruction, which is finally solved via the alternating minimization algorithm. Extensive experiments implemented on two representative systems demonstrate that our method outperforms state-of-the-art methods.

Thermal Image Enhancement Using Generative Adversarial Network for Pedestrian Detection

Mohamed Amine Marnissi, Hajer Fradi, Anis Sahbani, Najoua Essoukri Ben Amara

Auto-TLDR; Improving Visual Quality of Infrared Images for Pedestrian Detection Using Generative Adversarial Network

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Infrared imaging has recently played an important role in a wide range of applications including surveillance, robotics and night vision. However, infrared cameras often suffer from some limitations, essentially about low-contrast and blurred details. These problems contribute to the loss of observation of target objects in infrared images, which could limit the feasibility of different infrared imaging applications. In this paper, we mainly focus on the problem of pedestrian detection on thermal images. Particularly, we emphasis the need for enhancing the visual quality of images beforehand performing the detection step. % to ensure effective results. To address that, we propose a novel thermal enhancement architecture based on Generative Adversarial Network, and composed of two modules contrast enhancement and denoising modules with a post-processing step for edge restoration in order to improve the overall quality. The effectiveness of the proposed architecture is assessed by means of visual quality metrics and better results are obtained compared to the original thermal images and to the obtained results by other existing enhancement methods. These results have been conduced on a subset of KAIST dataset. Using the same dataset, the impact of the proposed enhancement architecture has been demonstrated on the detection results by obtaining better performance with a significant margin using YOLOv3 detector.

Video Reconstruction by Spatio-Temporal Fusion of Blurred-Coded Image Pair

Anupama S, Prasan Shedligeri, Abhishek Pal, Kaushik Mitr

Auto-TLDR; Recovering Video from Motion-Blurred and Coded Exposure Images Using Deep Learning

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Learning-based methods have enabled the recovery of a video sequence from a single motion-blurred image or a single coded exposure image. Recovering video from a single motion-blurred image is a very ill-posed problem and the recovered video usually has many artifacts. In addition to this, the direction of motion is lost and it results in motion ambiguity. However, it has the advantage of fully preserving the information in the static parts of the scene. The traditional coded exposure framework is better-posed but it only samples a fraction of the space-time volume, which is at best $50\%$ of the space-time volume. Here, we propose to use the complementary information present in the fully-exposed (blurred) image along with the coded exposure image to recover a high fidelity video without any motion ambiguity. Our framework consists of a shared encoder followed by an attention module to selectively combine the spatial information from the fully-exposed image with the temporal information from the coded image, which is then super-resolved to recover a non-ambiguous high-quality video. The input to our algorithm is a fully-exposed and coded image pair. Such an acquisition system already exists in the form of a Coded-two-bucket (C2B) camera. We demonstrate that our proposed deep learning approach using blurred-coded image pair produces much better results than those from just a blurred image or just a coded image.

Towards Artifacts-Free Image Defogging

Gabriele Graffieti, Davide Maltoni

Auto-TLDR; CurL-Defog: Learning Based Defogging with CycleGAN and HArD

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In this paper we present a novel defogging technique, named CurL-Defog, aimed at minimizing the creation of artifacts. The majority of learning based defogging approaches relies on paired data (i.e., the same images with and without fog), where fog is artificially added to clear images: this often provides good results on mildly fogged images but does not generalize well to real difficult cases. On the other hand, the models trained with real unpaired data (e.g. CycleGAN) can provide visually impressive results but often produce unwanted artifacts. In this paper we propose a curriculum learning strategy coupled with an enhanced CycleGAN model in order to reduce the number of produced artifacts, while maintaining state-of-the- art performance in terms of contrast enhancement and image reconstruction. We also introduce a new metric, called HArD (Hazy Artifact Detector) to numerically quantify the amount of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

Lightweight Low-Resolution Face Recognition for Surveillance Applications

Yoanna Martínez-Díaz, Heydi Mendez-Vazquez, Luis S. Luevano, Leonardo Chang, Miguel Gonzalez-Mendoza

Auto-TLDR; Efﬁciency of Lightweight Deep Face Networks on Low-Resolution Surveillance Imagery

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Typically, real-world requirements to deploy face recognition models in unconstrained surveillance scenarios demand to identify low-resolution faces with extremely low computational cost. In the last years, several methods based on complex deep learning models have been proposed with promising recognition results but at a high computational cost. Inspired by the compactness and computation efﬁciency of lightweight deep face networks and their high accuracy on general face recognition tasks, in this work we propose to benchmark two recently introduced lightweight face models on low-resolution surveillance imagery to enable efﬁcient system deployment. In this way, we conduct a comprehensive evaluation on the two typical settings: LR-to-HR and LR-to-LR matching. In addition, we investigate the effect of using trained models with down-sampled synthetic data from high-resolution images, as well as the combination of different models, for face recognition on real low-resolution images. Experimental results show that the used lightweight face models achieve state-of-the-art results on low-resolution benchmarks with low memory footprint and computational complexity. Moreover, we observed that combining models trained with different degradations improves the recognition accuracy on low-resolution surveillance imagery, which is feasible due to their low computational cost.

Semi-Supervised Deep Learning Techniques for Spectrum Reconstruction

Adriano Simonetto, Vincent Parret, Alexander Gatto, Piergiorgio Sartor, Pietro Zanuttigh

Auto-TLDR; hyperspectral data estimation from RGB data using semi-supervised learning

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State-of-the-art approaches for the estimation of hyperspectral images (HSI) from RGB data are mostly based on deep learning techniques but due to the lack of training data their performances are limited to uncommon scenarios where a large hyperspectral database is available. In this work we present a family of novel deep learning schemes for hyperspectral data estimation able to work when the hyperspectral information at our disposal is limited. Firstly, we introduce a learning scheme exploiting a physical model based on the backward mapping to the RGB space and total variation regularization that can be trained with a limited amount of HSI images. Then, we propose a novel semi-supervised learning scheme able to work even with just a few pixels labeled with hyperspectral information. Finally, we show that the approach can be extended to a transfer learning scenario. The proposed techniques allow to reach impressive performances while requiring only some HSI images or just a few pixels for the training.

Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search

Chu Xiangxiang, Bo Zhang, Micheal Ma Hailong, Ruijun Xu, Jixiang Li, Qingyuan Li

Auto-TLDR; Multi-Objective Neural Architecture Search for Super-Resolution

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Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.

Free-Form Image Inpainting Via Contrastive Attention Network

Xin Ma, Xiaoqiang Zhou, Huaibo Huang, Zhenhua Chai, Xiaolin Wei, Ran He

Auto-TLDR; Self-supervised Siamese inference for image inpainting

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Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representational spaces, which are important for dealing with sophisticated learning tasks. Specifically, in the image inpainting task, masks with any shapes can appear anywhere in images (i.e., free-form masks) forming complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. To tackle this problem, we propose a self-supervised Siamese inference network to improve the robustness and generalization. Moreover, the restored image usually can not be harmoniously integrated into the exiting content, especially in the boundary area. To address this problem, we propose a novel Dual Attention Fusion module (DAF), which can combine both the restored and known regions in a smoother way and be inserted into decoder layers in a plug-and-play way. DAF is developed to not only adaptively rescale channel-wise features by taking interdependencies between channels into account but also force deep convolutional neural networks (CNNs) focusing more on unknown regions. In this way, the unknown region will be naturally filled from the outside to the inside. Qualitative and quantitative experiments on multiple datasets, including facial and natural datasets (i.e., Celeb-HQ, Pairs Street View, Places2 and ImageNet), demonstrate that our proposed method outperforms against state-of-the-arts in generating high-quality inpainting results.

OCT Image Segmentation Using NeuralArchitecture Search and SRGAN

Saba Heidari, Omid Dehzangi, Nasser M. Nasarabadi, Ali Rezai

Auto-TLDR; Automatic Segmentation of Retinal Layers in Optical Coherence Tomography using Neural Architecture Search

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Alzheimer’s disease (AD) diagnosis is one of the major research areas in computational medicine. Optical coherence tomography (OCT) is a non-invasive, inexpensive, and timely efficient method that scans the human’s retina with depth. It has been hypothesized that the thickness of the retinal layers extracted from OCTs could be an efficient and effective biomarker for early diagnosis of AD. In this work, we aim to design a self-training model architecture for the task of segmenting the retinal layers in OCT scans. Neural architecture search (NAS) is a subfield of AutoML domain, which has a significant impact on improving the accuracy of machine vision tasks. We integrate the NAS algorithm with a Unet auto-encoder architecture as its backbone. Then, we employ our proposed model to segment the retinal nerve fiber layer in our preprocessed OCT images with the aim of AD diagnosis. In this work, we trained a super-resolution generative adversarial network on the raw OCT scans to improve the quality of the images before the modeling stage. In our architecture search strategy, different primitive operations suggested to find down- \& up-sampling Unet cell blocks and the binary gate method has been applied to make the search strategy more practical. Our architecture search method is empirically evaluated by training on the Unet and NAS-Unet from scratch. Specifically, the proposed NAS-Unet training significantly outperforms the baseline human-designed architecture by achieving 95.1\% in the mean Intersection over Union metric and 79.1\% in the Dice similarity coefﬁcient.

Video Lightening with Dedicated CNN Architecture

Li-Wen Wang, Wan-Chi Siu, Zhi-Song Liu, Chu-Tak Li, P. K. Daniel Lun

Auto-TLDR; VLN: Video Lightening Network for Driving Assistant Systems in Dark Environment

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Darkness brings us uncertainty, worry and low confidence. This is a problem not only applicable to us walking in a dark evening but also for drivers driving a car on the road with very dim or even without lighting condition. To address this problem, we propose a new CNN structure named as Video Lightening Network (VLN) that regards the low-light enhancement as a residual learning task, which is useful as reference to indirectly lightening the environment, or for vision-based application systems, such as driving assistant systems. The VLN consists of several Lightening Back-Projection (LBP) and Temporal Aggregation (TA) blocks. Each LBP block enhances the low-light frame by domain transfer learning that iteratively maps the frame between the low- and normal-light domains. A TA block handles the motion among neighboring frames by investigating the spatial and temporal relationships. Several TAs work in a multi-scale way, which compensates the motions at different levels. The proposed architecture has a consistent enhancement for different levels of illuminations, which significantly increases the visual quality even in the extremely dark environment. Extensive experimental results show that the proposed approach outperforms other methods under both objective and subjective metrics.

DSPNet: Deep Learning-Enabled Blind Reduction of Speckle Noise

Yuxu Lu, Meifang Yang, Liu Wen

Auto-TLDR; Deep Blind DeSPeckling Network for Imaging Applications

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Blind reduction of speckle noise has become a long-standing unsolved problem in several imaging applications, such as medical ultrasound imaging, synthetic aperture radar (SAR) imaging, and underwater sonar imaging, etc. The unwanted noise could lead to negative effects on the reliable detection and recognition of objects of interest. From a statistical point of view, speckle noise could be assumed to be multiplicative, significantly different from the common additive Gaussian noise. The purpose of this study is to blindly reduce the speckle noise under non-ideal imaging conditions. The multiplicative relationship between latent sharp image and random noise will be first converted into an additive version through a logarithmic transformation. To promote imaging performance, we introduced the feature pyramid network (FPN) and atrous spatial pyramid pooling (ASPP), contributing to a more powerful deep blind DeSPeckling Network (named as DSPNet). In particular, DSPNet is mainly composed of two subnetworks, i.e., Log-NENet (i.e., noise estimation network in logarithmic domain) and Log-DNNet (i.e., denoising network in logarithmic domain). Log-NENet and Log-DNNet are, respectively, proposed to estimate noise level map and reduce random noise in logarithmic domain. The multi-scale mixed loss function is further proposed to improve the robust generalization of DSPNet. The proposed deep blind despeckling network is capable of reducing random noise and preserving salient image details. Both synthetic and realistic experiments have demonstrated the superior performance of our DSPNet in terms of quantitative evaluations and visual image qualities.

Ancient Document Layout Analysis: Autoencoders Meet Sparse Coding

Homa Davoudi, Marco Fiorucci, Arianna Traviglia

Auto-TLDR; Unsupervised Unsupervised Representation Learning for Document Layout Analysis

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Layout analysis of historical handwritten documents is a key pre-processing step in document image analysis that, by segmenting the image into its homogeneous regions, facilitates subsequent procedures such as optical character recognition and automatic transcription. Learning-based approaches have shown promising performances in layout analysis, however, the majority of them requires tedious pixel-wise labelled training data to achieve generalisation capabilities, this limitation preventing their application due to the lack of large labelled datasets. This paper proposes a novel unsupervised representation learning method for documents’ layout analysis that reduces the need for labelled data: a sparse autoencoder is first trained in an unsupervised manner on a historical text document’s image; representation of image patches, computed by the sparse encoder, is then used to classify pixels into various region categories of the document using a feed-forward neural network. A new training method, inspired by the ISTA algorithm, is also introduced here to train the sparse encoder. Experimental results on DIVA-HisDB dataset demonstrate that the proposed method outperforms previous approaches based on unsupervised representation learning while achieving performances comparable to the state-of-the-art fully supervised methods.

Boundary Guided Image Translation for Pose Estimation from Ultra-Low Resolution Thermal Sensor

Kohei Kurihara, Tianren Wang, Teng Zhang, Brian Carrington Lovell

Auto-TLDR; Pose Estimation on Low-Resolution Thermal Images Using Image-to-Image Translation Architecture

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This work addresses the pose estimation task on low-resolution images captured using thermal sensors which can operate in a no-light environment. Low-resolution thermal sensors have been widely adopted in various applications for cost control and privacy protection purposes. In this paper, targeting the challenging scenario of ultra-low resolution thermal imaging (3232 pixels), we aim to estimate human poses for the purpose of monitoring health conditions and indoor events. To overcome the challenges in ultra-low resolution thermal imaging such as blurred boundaries and data scarcity, we propose a new Image-to-Image (I2I) translation architecture which can translate the original blurred thermal image into a visible light image with sharper boundaries. Then the generated visible light image can be fed into the off-the-shelf pose estimator which was well-trained in the visible domain. Experimental results suggest that the proposed framework outperforms other state-of-the-art methods in the I2I based pose estimation task for our thermal image dataset. Furthermore, we also demonstrated the merits of the proposed method on the publicly available FLIR dataset by measuring the quality of translated images.

Multi-scale Processing of Noisy Images using Edge Preservation Losses

Nati Ofir

Auto-TLDR; Multi-scale U-net for Noisy Image Detection and Denoising

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Noisy image processing is a fundamental task of computer vision. The first example is the detection of faint edges in noisy images, a challenging problem studied in the last decades. A recent study introduced a fast method to detect faint edges in the highest accuracy among all the existing approaches. Their complexity is nearly linear in the image's pixels and their runtime is seconds for a noisy image. Their approach utilizes a multi-scale binary partitioning of the image. By utilizing the multi-scale U-net architecture, we show in this paper that their method can be dramatically improved in both aspects of run time and accuracy. By training the network on a dataset of binary images, we developed an approach for faint edge detection that works in linear complexity. Our runtime of a noisy image is milliseconds on a GPU. Even though our method is orders of magnitude faster, we still achieve higher accuracy of detection under many challenging scenarios. In addition, we show that our approach to performing multi-scale preprocessing of noisy images using U-net improves the ability to perform other vision tasks under the presence of noise. We prove it on the problems of noisy objects classification and classical image denoising. We show that multi-scale denoising can be carried out by a novel edge preservation loss. As our experiments show, we achieve high-quality results in the three aspects of faint edge detection, noisy image classification and natural image denoising.