Single-Image Super-Resolution by Subdictionary Coding and Kernel Regression

Abstract

In this paper, we present a new learning-based single-image super-resolution (SR) approach, inspired by existing sparse representation-based methods. As a promising image modeling theory, sparse representation has been effectively applied to solve the image SR problem, usually with the use of pretrained coupled or semi-coupled dictionaries. In our proposed method, we train independent dictionaries for high-resolution (HR) and low-resolution (LR) image patches to endow them more flexibility of expression. We use local subdictionaries to adaptively code image patches, which can characterize image local structures better and ensure the sparsity property of the image. Furthermore, we use kernel regression to relate HR and LR coding coefficients to capture and map the intrinsic nonlinear relationship between them. Such mapping is of central importance in the image SR problem, because high-order statistics play a significant role in the reconstruction of the detail structure of an HR image. The proposed model is generic for image SR in terms of two categories of blurring kernel. Experimental results show that our method can effectively reconstruct image details and outperform state-of-the-art algorithms in both quantitative and visual comparisons.