CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network
This work addresses image super-resolution for computer vision applications, presenting an incremental improvement by adapting existing CSC methods into a CNN framework.
The paper tackles the image super-resolution problem by proposing a convolutional sparse coding inspired network (CRNet) that bridges CSC and CNNs, achieving superior performance over recent state-of-the-art methods in quantitative and qualitative measurements.
Convolutional Sparse Coding (CSC) has been attracting more and more attention in recent years, for making full use of image global correlation to improve performance on various computer vision applications. However, very few studies focus on solving CSC based image Super-Resolution (SR) problem. As a consequence, there is no significant progress in this area over a period of time. In this paper, we exploit the natural connection between CSC and Convolutional Neural Networks (CNN) to address CSC based image SR. Specifically, Convolutional Iterative Soft Thresholding Algorithm (CISTA) is introduced to solve CSC problem and it can be implemented using CNN architectures. Then we develop a novel CSC based SR framework analogy to the traditional SC based SR methods. Two models inspired by this framework are proposed for pre-/post-upsampling SR, respectively. Compared with recent state-of-the-art SR methods, both of our proposed models show superior performance in terms of both quantitative and qualitative measurements.