Image Super-Resolution Reconstruction Using Adaptive Co-sparse Regularization with Dual Dictionary

Abstract

Abstract-This paper present a new method based on co-sparse with learning paired dictionary. The new framework is consisted of three parts. Firstly a paired dictionary have been learned which is used to overcome a low resolution image by utilizing an externally applied high resolution (HR) dictionary and then learn based on the internal dictionary. Process the paired dictionary which consists of low resolution (LR) and high resolution (HR) dictionary by kernel regression based on their coefficient respectively, and applied directly to construct the HR patches. Secondly, co-sparse regularization and features of self similarity have been introduced to strengthen and enhanced the image structure. In addition, propagation filtering is applied to suppress the artefacts generated from neighboring pixel of an image while reserving the image edges. Finally, the HR image is generated by reconstructing all superior HR patches. The effectiveness of the co-sparse demonstrated in real test images. The proposed method achieved good quality high resolution images that are superior compared with different SR methods in terms of peak signal to noise ratio (PSNR), and structural similarity (SSIM). IndexTerms-Dual dictionary, image resolution, propagation filter, self-similarity.