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.

Along with the performance improvements of neural network models, developing methods that enable the explanation of their behavior is a significant research topic. For convolutional neural networks, the explainability is usually achieved with attribution (heatmap) that visualizes pixel-level importance or contribution of input to its corresponding result. This attribution should reflect the relation (dependency) between inputs and outputs, which has been studied with a variety of methods, e.g., derivative of an output with respect to an input pixel value, a weighted sum of gradients, amount of output changes to input perturbations, and so on. In this paper, we present a new method that improves the measure of attribution, and incorporates it into the integrated gradients method. To be precise, rather than using the conventional chain-rule, we propose a method called guided non-linearity that propagates gradients more effectively through non-linear units (e.g., ReLU and max-pool) so that only positive gradients backpropagate through non-linear units. Our method is inspired by the mechanism of action potential generation in postsynaptic neurons, where the firing of action potentials depends on the sum of excitatory (EPSP) and inhibitory postsynaptic potentials (IPSP). We believe that paths consisting of EPSP-giving-neurons faithfully reflect the contribution of inputs to the output, and we make gradients flow only along those paths (i.e., paths of positive chain reactions). Experiments with 5 deep neural networks have shown that the proposed method outperforms others in terms of the deletion metrics, and yields fine-grained and more human-interpretable attribution.

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 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.

Since gaze estimation plays a crucial role in recognizing human intentions, it has been researched for a long time, and its accuracy is ever increasing. However, due to the wide variation in eye shapes and focusing abilities between the individuals, accuracies of most algorithms vary depending on each person in the test group, especially when the initial calibration is not well performed. To alleviate the user-dependency, we attempt to derive features that are general for most people and use them as the input to a deep network instead of using the images as the input. Specifically, we use the pupil shape as the core feature because it is directly related to the 3D eyeball rotation, and thus the gaze direction. While existing deep learning methods learn the gaze point by extracting various features from the image, we focus on the mapping function from the eyeball rotation to the gaze point by using the pupil shape as the input. It is shown that the accuracy of gaze point estimation also becomes robust for the uncalibrated points by following the characteristics of the mapping function. Also, our gaze network learns the gaze difference to facilitate the re-calibration process to fix the calibration-drift problem that typically occurs with glass-type or head-mount devices.