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Convolutional Sparse Coding (CSC) has been attracting more and more attentionin recent years, for making full use of image global correlation to improveperformance on various computer vision applications. However, very few studiesfocus on solving CSC based image Super-Resolution (SR) problem. As aconsequence, there is no significant progress in this area over a period oftime. In this paper, we exploit the natural connection between CSC andConvolutional Neural Networks (CNN) to address CSC based image SR.Specifically, Convolutional Iterative Soft Thresholding Algorithm (CISTA) isintroduced to solve CSC problem and it can be implemented using CNNarchitectures. Then we develop a novel CSC based SR framework analogy to thetraditional SC based SR methods. Two models inspired by this framework areproposed for pre-/post-upsampling SR, respectively. Compared with recentstate-of-the-art SR methods, both of our proposed models show superiorperformance in terms of both quantitative and qualitative measurements.

CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network