Fast Single-Image Super-Resolution via Deep Network With Component Learning

Abstract

Driven by the spectacular success of deep learning, several advanced models based on neural networks have recently been proposed for single-image super-resolution, incrementally revealing their superiority over their alternatives. In this paper, we pursue this latest line of research and present an improved network structure by taking advantage of the proposed component learning. The core idea and difference of this learning strategy are to use the residual extracted from the input to predict its counterpart in the corresponding output. To this end, a global decomposition procedure is designed on the basis of convolutional sparse coding and performed on the input for extracting the low-resolution (LR) residual component from it. Owing to the properties of this decomposition, the represented residual component still stays in the LR space so that the subsequent part is capable of operating it economically in terms of computational complexity. Thorough experimental results demonstrate the merit and effectiveness of the proposed component learning strategy, and our trained model outperforms many state-of-the-art methods in terms of both speed and reconstruction quality.