High Resolution Image Reconstruction via Dictionary Learning in Sparse Environment

Abstract

In this paper, single- image super-resolution framework is presented by means of sparse representation. Patch-based super resolution is a technique where spatial features from a low-resolution (LR) patches are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation for each patch is extracted and these coefficients are used to recover super-resolution patch. Two dictionaries are jointly trained for the LR and HR image patches. By this, similarity between sparse representation between the LR and HR patch pair is established. Hence, the sparse representation of a LR image patch can be applied with the HR image patch dictionary to obtain a HR image patch. The dictionary thus learnt is a compact one. The experimental results demonstrate the effectiveness of the proposed algorithm.