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We propose a simple yet effective model for Single Image Super-Resolution(SISR), by combining the merits of Residual Learning and Convolutional SparseCoding (RL-CSC). Our model is inspired by the Learned IterativeShrinkage-Threshold Algorithm (LISTA). We extend LISTA to its convolutionalversion and build the main part of our model by strictly following theconvolutional form, which improves the network's interpretability.Specifically, the convolutional sparse codings of input feature maps arelearned in a recursive manner, and high-frequency information can be recoveredfrom these CSCs. More importantly, residual learning is applied to alleviatethe training difficulty when the network goes deeper. Extensive experiments onbenchmark datasets demonstrate the effectiveness of our method. RL-CSC (30layers) outperforms several recent state-of-the-arts, e.g., DRRN (52 layers)and MemNet (80 layers) in both accuracy and visual qualities. Codes and moreresults are available at https://github.com/axzml/RL-CSC.

Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding