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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

Abstract Slides Poster

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.

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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

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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.

T-SVD Based Non-Convex Tensor Completion and Robust Principal Component Analysis

Tao Li, Jinwen Ma

Auto-TLDR; Non-Convex tensor rank surrogate function and non-convex sparsity measure for tensor recovery

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In this paper, we propose a novel non-convex tensor rank surrogate function and a novel non-convex sparsity measure. The basic idea is to sidestep the bias of $\ell_1-$norm by introducing the concavity. Furthermore, we employ this non-convex penalty in tensor recovery problems such as tensor completion and tensor robust principal component analysis. Due to the concavity, the parameters of these models are difficult to solve. To tackle this problem, we devise a majorization minimization algorithm that can optimize the upper bound of the original function in each iteration, and every sub-problem is solved by the alternating direction multiplier method. We also analyze the theoretical properties of the proposed algorithm. Finally, the experimental results on natural and hyperspectral images demonstrate the efficacy and efficiency of the proposed method.

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.

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.

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 Similar

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.

Tensorized Feature Spaces for Feature Explosion

Ravdeep Pasricha, Pravallika Devineni, Evangelos Papalexakis, Ramakrishnan Kannan

Auto-TLDR; Tensor Rank Decomposition for Hyperspectral Image Classification

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In this paper, we present a novel framework that uses tensor factorization to generate richer feature spaces for pixel classification in hyperspectral images. In particular, we assess the performance of different tensor rank decomposition methods as compared to the traditional kernel-based approaches for the hyperspectral image classification problem. We propose ORION, which takes as input a hyperspectral image tensor and a rank and outputs an enhanced feature space from the factor matrices of the decomposed tensor. Our method is a feature explosion technique that inherently maps low dimensional input space in R^K to high dimensional space in R^R, where R >> K, say in the order of 1000x, like a kernel. We show how the proposed method exploits the multi-linear structure of hyperspectral three-dimensional tensor. We demonstrate the effectiveness of our method with experiments on three publicly available hyperspectral datasets with labeled pixels and compare their classification performance against traditional linear and non-linear supervised learning methods such as SVM with Linear, Polynomial, RBF kernels, and the Multi-Layer Perceptron model. Finally, we explore the relationship between the rank of the tensor decomposition and the classification accuracy using several hyperspectral datasets with ground truth.

A Spectral Clustering on Grassmann Manifold Via Double Low Rank Constraint

Xinglin Piao, Yongli Hu, Junbin Gao, Yanfeng Sun, Xin Yang, Baocai Yin

Auto-TLDR; Double Low Rank Representation for High-Dimensional Data Clustering on Grassmann Manifold

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High-dimension data clustering is a fundamental topic in machine learning and data mining areas. In recent year, researchers have proposed a series of effective methods based on Low Rank Representation (LRR) which could explore low-dimension subspace structure embedded in original data effectively. The traditional LRR methods usually treat original data as samples in Euclidean space. They generally adopt linear metric to measure the distance between two data. However, high-dimension data (such as video clip or imageset) are always considered as non-linear manifold data such as Grassmann manifold. Therefore, the traditional linear Euclidean metric would be no longer suitable for these special data. In addition, traditional LRR clustering method always adopt nuclear norm as low rank constraint which would lead to suboptimal solution and decrease the clustering accuracy. In this paper, we proposed a new low rank method on Grassmann manifold for high-dimension data clustering task. In the proposed method, a double low rank representation approach is proposed by combining the nuclear norm and bilinear representation for better construct the representation matrix. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods.

Fast Subspace Clustering Based on the Kronecker Product

Lei Zhou, Xiao Bai, Liang Zhang, Jun Zhou, Edwin Hancock

Auto-TLDR; Subspace Clustering with Kronecker Product for Large Scale Datasets

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Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N^3) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN^{3/k}). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

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.

Embedding Shared Low-Rank and Feature Correlation for Multi-View Data Analysis

Zhan Wang, Lizhi Wang, Hua Huang

Auto-TLDR; embedding shared low-rank and feature correlation for multi-view data analysis

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The diversity of multimedia data in the real-world usually forms multi-view features. How to explore the structure information and correlations among multi-view features is still an open problem. In this paper, we propose a novel multi-view subspace learning method, named embedding shared low-rank and feature correlation (ESLRFC), for multi-view data analysis. First, in the embedding subspace, we propose a robust low-rank model on each feature set and enforce a shared low-rank constraint to characterize the common structure information of multiple feature data. Second, we develop an enhanced correlation analysis in the embedding subspace for simultaneously removing the redundancy of each feature set and exploring the correlations of multiple feature data. Finally, we incorporate the low-rank model and the correlation analysis into a unified framework. The shared low-rank constraint not only depicts the data distribution consistency among multiple feature data, but also assists robust subspace learning. Experimental results on recognition tasks demonstrate the superior performance and noise robustness of the proposed method.

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

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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).

Low Rank Representation on Product Grassmann Manifolds for Multi-viewSubspace Clustering

Jipeng Guo, Yanfeng Sun, Junbin Gao, Yongli Hu, Baocai Yin

Auto-TLDR; Low Rank Representation on Product Grassmann Manifold for Multi-View Data Clustering

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Clustering high dimension multi-view data with complex intrinsic properties and nonlinear manifold structure is a challenging task since these data are always embedded in low dimension manifolds. Inspired by Low Rank Representation (LRR), some researchers extended classic LRR on Grassmann manifold or Product Grassmann manifold to represent data with non-linear metrics. However, most of these methods utilized convex nuclear norm to leverage a low-rank structure, which was over-relaxation of true rank and would lead to the results deviated from the true underlying ones. And, the computational complexity of singular value decomposition of matrix is high for nuclear norm minimization. In this paper, we propose a new low rank model for high-dimension multi-view data clustering on Product Grassmann Manifold with the matrix tri-factorization which is used to control the upper bound of true rank of representation matrix. And, the original problem can be transformed into the nuclear norm minimization with smaller scale matrices. An effective solution and theoretical analysis are also provided. The experimental results show that the proposed method obviously outperforms other state-of-the-art methods on several multi-source human/crowd action video datasets.

Multi-Scanning Based Recurrent Neural Network for Hyperspectral Image Classification

Weilian Zhou, Sei-Ichiro Kamata

Auto-TLDR; Spatial-Spectral Unification for Hyperspectral Image Classification

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As the specialty of hyperspectral image (HSI), it consists of 2D spatial and 1D spectral information. In the field of deep learning, HSI classification is an appealing research topic. Many existing methods process the HSI in spatial or spectral domain separately, which cannot fully extract the representative features and the most used 3D convolutional neural network (3D-CNN) will suffer from mixing up complex spectral information. In this paper, we propose a spatial-spectral unified method by using recurrent neural networks (RNN) and multi-scanning direction strategy to construct spatial-spectral information sequences for learning the spatial dependencies among the central pixel and neighboring pixels. Meanwhile, residual connections and dense connections are introduced into multi-scanning direction sequences to overcome the memory problem in the RNN. The proposed method is tested on two benchmark datasets: the Pavia University dataset and the Pavia Center dataset. The experimental results demonstrate that the proposed method can achieve better classification rate than other state-of-the-art methods.

Subspace Clustering Via Joint Unsupervised Feature Selection

Wenhua Dong, Xiaojun Wu, Hui Li, Zhenhua Feng, Josef Kittler

Auto-TLDR; Unsupervised Feature Selection for Subspace Clustering

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Any high-dimensional data arising from practical applications usually contains irrelevant features, which may impact on the performance of existing subspace clustering methods. This paper proposes a novel subspace clustering method, which reconstructs the feature matrix by the means of unsupervised feature selection (UFS) to achieve a better dictionary for subspace clustering (SC). Different from most existing clustering methods, the proposed approach uses a reconstructed feature matrix as the dictionary rather than the original data matrix. As the feature matrix reconstructed by representative features is more discriminative and closer to the ground-truth, it results in improved performance. The corresponding non-convex optimization problem is effectively solved using the half-quadratic and augmented Lagrange multiplier methods. Extensive experiments on four real datasets demonstrate the effectiveness of the proposed method.

CausalX: Causal Explanations and Block Multilinear Factor Analysis

M. Alex O. Vasilescu, Eric Kim, Xiao. S. Zeng

Auto-TLDR; Unified tensor model of wholes and parts for object image formation

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Objects and activities (temporal objects) are composed of a recursive hierarchy of perceptual wholes and parts, whose properties, such as shape, reflectance, and color, constitute a hierarchy of intrinsic causal factors of object appearance.However, object appearance is the compositional consequence of both an object’s intrinsic and extrinsic causal factors, where the extrinsic causal factors are related to illumination, and imaging conditions.We propose a unified tensor model of wholes and parts that statistically models the mechanism of data formation. We derive a compositional hierarchical block tensor factorization that computes a disentangled representation of the causal factors of object image formation by optimizing simultaneously across wholes and parts. Given computational efficiency considerations,we introduce an incremental bottom-up computational alternative that employs the lower level part representations to represent the higher level of abstractions, the parent wholes. This incremental computational approach may also be employed to update the causal model representation when data that becomes available incrementally. The resulting object representation is an interpretable combinatorial choice of intrinsic causal factor representations related to an object’s recursive hierarchy of wholes and parts that renders object recognition robust to occlusion and reduces training data requirements.

Double Manifolds Regularized Non-Negative Matrix Factorization for Data Representation

Jipeng Guo, Shuai Yin, Yanfeng Sun, Yongli Hu

Auto-TLDR; Double Manifolds Regularized Non-negative Matrix Factorization for Clustering

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Non-negative matrix factorization (NMF) is an important method in learning latent data representation. The local geometrical structure can make the learned representation more effectively and significantly improve the performance of NMF. However, most of existing graph-based learning methods are determined by a predefined similarity graph which may be not optimal for specific tasks. To solve the above the problem, we propose the Double Manifolds Regularized NMF (DMR-NMF) model which jointly learns an adaptive affinity matrix with the non-negative matrix factorization. The learned affinity matrix can guide the NMF to fit the clustering task. Moreover, we develop the iterative updating optimization schemes for DMR-NMF, and provide the strict convergence proof of our optimization strategy. Empirical experiments on four different real-world data sets demonstrate the state-of-the-art performance of DMR-NMF in comparison with the other related algorithms.

Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets

Sumit Laha, Ankit Sharma, Shengnan Hu, Hassan Foroosh

Auto-TLDR; A fusion algorithm for haze removal using Haar wavelets

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We propose a fusion algorithm for haze removal that combines color information from an RGB image and edge information extracted from its corresponding NIR image using Haar wavelets. The proposed algorithm is based on the key observation that NIR edge features are more prominent in the hazy regions of the image than the RGB edge features in those same regions. To combine the color and edge information, we introduce a haze-weight map which proportionately distributes the color and edge information during the fusion process. Because NIR images are, intrinsically, nearly haze-free, our work makes no assumptions like existing works that rely on a scattering model and essentially designing a depth-independent method. This helps in minimizing artifacts and gives a more realistic sense to the restored haze-free image. Extensive experiments show that the proposed algorithm is both qualitatively and quantitatively better on several key metrics when compared to existing state-of-the-art 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

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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.

Extending Single Beam Lidar to Full Resolution by Fusing with Single Image Depth Estimation

Yawen Lu, Yuxing Wang, Devarth Parikh, Guoyu Lu

Auto-TLDR; Self-supervised LIDAR for Low-Cost Depth Estimation

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Depth estimation is playing an important role in indoor and outdoor scene understanding, autonomous driving, augmented reality and many other tasks. Vehicles and robotics are able to use active illumination sensors such as LIDAR to receive high precision depth estimation. However, high-resolution Lidars are usually too expensive, which limits its massive production on various applications. Though single beam LIDAR enjoys the benefits of low cost, one beam depth sensing is not usually sufficient to perceive the surrounding environment in many scenarios. In this paper, we propose a learning-based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR. After the accurate calibration with a visible camera, the single beam LIDAR can adjust the scale uncertainty of the depth map estimated by the visible camera. The adjusted depth map enjoys the benefits of high resolution and sensing accuracy as high beam LIDAR and maintains low-cost as single beam LIDAR. Thus we can achieve similar sensing effect of high beam LIDAR with more than a 50-100 times cheaper price (e.g., \$80000 Velodyne HDL-64E LIDAR v.s. \$1000 SICK TIM-781 2D LIDAR and normal camera). The proposed approach is verified on our collected dataset and public dataset with superior depth-sensing performance.

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

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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).

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

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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).

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

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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.

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

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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.

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

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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.

Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

Auto-TLDR; Recovering 3D Head Geometry from a Single Image using Deep Learning and Geometric Techniques

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Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

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

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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.

Joint Compressive Autoencoders for Full-Image-To-Image Hiding

Xiyao Liu, Ziping Ma, Xingbei Guo, Jialu Hou, Lei Wang, Gerald Schaefer, Hui Fang

Auto-TLDR; J-CAE: Joint Compressive Autoencoder for Image Hiding

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Image hiding has received significant attention due to the need of enhanced multimedia services, such as multimedia security and meta-information embedding for multimedia augmentation. Recently, deep learning-based methods have been introduced that are capable of significantly increasing the hidden capacity and supporting full size image hiding. However, these methods suffer from the necessity to balance the errors of the modified cover image and the recovered hidden image. In this paper, we propose a novel joint compressive autoencoder (J-CAE) framework to design an image hiding algorithm that achieves full-size image hidden capacity with small reconstruction errors of the hidden image. More importantly, it addresses the trade-off problem of previous deep learning-based methods by mapping the image representations in the latent spaces of the joint CAE models. Thus, both visual quality of the container image and recovery quality of the hidden image can be simultaneously improved. Extensive experimental results demonstrate that our proposed framework outperforms several state-of-the-art deep learning-based image hiding methods in terms of imperceptibility and recovery quality of the hidden images while maintaining full-size image hidden capacity.

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.

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

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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.

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

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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.

Joint Learning Multiple Curvature Descriptor for 3D Palmprint Recognition

Lunke Fei, Bob Zhang, Jie Wen, Chunwei Tian, Peng Liu, Shuping Zhao

Auto-TLDR; Joint Feature Learning for 3D palmprint recognition using curvature data vectors

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3D palmprint-based biometric recognition has drawn growing research attention due to its several merits over 2D counterpart such as robust structural measurement of a palm surface and high anti-counterfeiting capability. However, most existing 3D palmprint descriptors are hand-crafted that usually extract stationary features from 3D palmprint images. In this paper, we propose a feature learning method to jointly learn compact curvature feature descriptor for 3D palmprint recognition. We first form multiple curvature data vectors to completely sample the intrinsic curvature information of 3D palmprint images. Then, we jointly learn a feature projection function that project curvature data vectors into binary feature codes, which have the maximum inter-class variances and minimum intra-class distance so that they are discriminative. Moreover, we learn the collaborative binary representation of the multiple curvature feature codes by minimizing the information loss between the final representation and the multiple curvature features, so that the proposed method is more compact in feature representation and efficient in matching. Experimental results on the baseline 3D palmprint database demonstrate the superiority of the proposed method in terms of recognition performance in comparison with state-of-the-art 3D palmprint descriptors.

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.

Deep Universal Blind Image Denoising

Jae Woong Soh, Nam Ik Cho

Auto-TLDR; Image Denoising with Deep Convolutional Neural Networks

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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.

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.

A Dual-Branch Network for Infrared and Visible Image Fusion

Yu Fu, Xiaojun Wu

Auto-TLDR; Image Fusion Using Autoencoder for Deep Learning

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In recent years, deep learning has been used extensively in the field of image fusion. In this article, we propose a new image fusion method by designing a new structure and a new loss function for a deep learning model. Our backbone network is an autoencoder, in which the encoder has a dual branch structure. We input infrared images and visible light images to the encoder to extract detailed information and semantic information respectively. The fusion layer fuses two sets of features to get fused features. The decoder reconstructs the fusion features to obtain the fused image. We design a new loss function to reconstruct the image effectively. Experiments show that our proposed method achieves state-of-the-art performance.

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.

Learning Non-Rigid Surface Reconstruction from Spatio-Temporal Image Patches

Matteo Pedone, Abdelrahman Mostafa, Janne Heikkilä

Auto-TLDR; Dense Spatio-Temporal Depth Maps of Deformable Objects from Video Sequences

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We present a method to reconstruct a dense spatio-temporal depth map of a non-rigidly deformable object directly from a video sequence. The estimation of depth is performed locally on spatio-temporal patches of the video, and then the full depth video of the entire shape is recovered by combining them together. Since the geometric complexity of a local spatio-temporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using other approaches like conventional non-rigid structure from motion.

DR2S: Deep Regression with Region Selection for Camera Quality Evaluation

Marcelin Tworski, Stéphane Lathuiliere, Salim Belkarfa, Attilio Fiandrotti, Marco Cagnazzo

Auto-TLDR; Texture Quality Estimation Using Deep Learning

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In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition. Importantly, our texture preservation measurement should coincide with human perception. Consequently, we formulate our problem as a regression one and we introduce a deep convolutional network to estimate texture quality score. At training time, we use ground-truth quality scores provided by expert human annotators in order to obtain a subjective quality measure. In addition, we propose a region selection method to identify the image regions that are better suited at measuring perceptual quality. Finally, our experimental evaluation shows that our learning-based approach outperforms existing methods and that our region selection algorithm consistently improves the quality estimation.

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

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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.

GraphBGS: Background Subtraction Via Recovery of Graph Signals

Jhony Heriberto Giraldo Zuluaga, Thierry Bouwmans

Auto-TLDR; Graph BackGround Subtraction using Graph Signals

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Background subtraction is a fundamental pre-processing task in computer vision. This task becomes challenging in real scenarios due to variations in the background for both static and moving camera sequences. Several deep learning methods for background subtraction have been proposed in the literature with competitive performances. However, these models show performance degradation when tested on unseen videos; and they require huge amount of data to avoid overfitting. Recently, graph-based algorithms have been successful approaching unsupervised and semi-supervised learning problems. Furthermore, the theory of graph signal processing and semi-supervised learning have been combined leading to new insights in the field of machine learning. In this paper, concepts of recovery of graph signals are introduced in the problem of background subtraction. We propose a new algorithm called Graph BackGround Subtraction (GraphBGS), which is composed of: instance segmentation, background initialization, graph construction, graph sampling, and a semi-supervised algorithm inspired from the theory of recovery of graph signals. Our algorithm has the advantage of requiring less data than deep learning methods while having competitive results on both: static and moving camera videos. GraphBGS outperforms unsupervised and supervised methods in several challenging conditions on the publicly available Change Detection (CDNet2014), and UCSD background subtraction databases.

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

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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.

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

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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.

Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks

Jie Ran, Rui Lin, Hayden Kwok-Hay So, Graziano Chesi, Ngai Wong

Auto-TLDR; Nuclear-Norm Rank Minimization Factorization for Deep Neural Networks

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Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs). Recognizing that the kernels in a convolutional neural network (CNN) are 4-way tensors, we further exploit a new elasticity dimension along the input-output channels. Specifically, a novel nuclear-norm rank minimization factorization (NRMF) approach is proposed to dynamically and globally search for the reduced tensor ranks during training. Correlation between tensor ranks across multiple layers is revealed, and a graceful tradeoff between model size and accuracy is obtained. Experiments then show the superiority of NRMF over the previous non-elastic variational Bayesian matrix factorization (VBMF) scheme.

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

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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.

Webly Supervised Image-Text Embedding with Noisy Tag Refinement

Niluthpol Mithun, Ravdeep Pasricha, Evangelos Papalexakis, Amit Roy-Chowdhury

Auto-TLDR; Robust Joint Embedding for Image-Text Retrieval Using Web Images

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In this paper, we address the problem of utilizing web images in training robust joint embedding models for the image-text retrieval task. Prior webly supervised approaches directly leverage weakly annotated web images in the joint embedding learning framework. The objective of these approaches would suffer significantly when the ratio of noisy and missing tags associated with the web images is very high. In this regard, we propose a CP decomposition based tensor completion framework to refine the tags of web images by modeling observed ternary inter-relations between the sets of labeled images, tags, and web images as a tensor. To effectively deal with the high ratio of missing entries likely in our case, we incorporate intra-modal correlation as side information in the proposed framework. Our tag refinement approach combined with existing web supervised image-text embedding approaches provide a more principled way for learning the joint embedding models in the presence of significant noise from web data and limited clean labeled data. Experiments on benchmark datasets demonstrate that the proposed approach helps to achieve a significant performance gain in image-text retrieval.

Visual Saliency Oriented Vehicle Scale Estimation

Qixin Chen, Tie Liu, Jiali Ding, Zejian Yuan, Yuanyuan Shang

Auto-TLDR; Regularized Intensity Matching for Vehicle Scale Estimation with salient object detection

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Vehicle scale estimation with a single camera is a typical application for intelligent transportation and it faces the challenges from visual computing while intensity-based method and descriptor-based method should be balanced. This paper proposed a vehicle scale estimation method based on salient object detection to resolve this problem. The regularized intensity matching method is proposed in Lie Algebra to achieve robust and accurate scale estimation, and descriptor matching and intensity matching are combined to minimize the proposed loss function. The visual attention mechanism is designed to select image patches with texture and remove the occluded image patches. Then the weights are assigned to pixels from the selected image patches which alleviates the influence of noise-corrupted pixels. The experiments show that the proposed method significantly outperforms state-of-the-art methods with regard to the robustness and accuracy of vehicle scale estimation.

Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Olivier Moliner, Sangxia Huang, Kalle Åström

Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

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Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

5D Light Field Synthesis from a Monocular Video

Kyuho Bae, Andre Ivan, Hajime Nagahara, In Kyu Park

Auto-TLDR; Synthesis of Light Field Video from Monocular Video using Deep Learning

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Commercially available light field cameras have difficulty in capturing 5D (4D + time) light field videos. They can only capture still light filed images or are excessively expensive for normal users to capture the light field video. To tackle this problem, we propose a deep learning-based method for synthesizing a light field video from a monocular video. We propose a new synthetic light field video dataset that renders photorealistic scenes using Unreal Engine because no light field video dataset is available. The proposed deep learning framework synthesizes the light field video with a full set (9x9) of sub-aperture images from a normal monocular video. The proposed network consists of three sub-networks, namely, feature extraction, 5D light field video synthesis, and temporal consistency refinement. Experimental results show that our model can successfully synthesize the light field video for synthetic and real scenes and outperforms the previous frame-by-frame method quantitatively and qualitatively.