Super-resolution for medical image via sparse representation and adaptive M-estimator

Abstract

Objective: The goal of super-resolution is to generate high-resolution images from low-resolution input images.Methods: In this paper, a combined method based on sparse signal representation and adaptive M-estimator is proposed for single-image super-resolution. With the sparse signal representation, the correlation between the sparse representation of high-resolution patches and that of low-resolution patches for the identical image is learned as a set of joint dictionaries and a set of high-resolution patches is obtained for high- and low-resolution patches. Then the dictionaries and high-resolution patches are used to produce the high-resolution image for a low-resolution single image.Results: At the post-processing phase, the adaptive M-estimator, combining the advantages of traditional L1 and L2 norms, is used to give further processing for the resultant high-resolution image, to reduce the artefact by learning and reconstitution, and improve the performance. Conclusion: Three experimental results show the performance improvement of the proposed algorithm over other methods.