Multi-layer strategy and reconstruction model with low rank and local rank regularizations for single image super-resolution

Abstract

Low rank method has been proven to be a powerful tool for image reconstruction. However, because of being hard to find sufficient similar patches for those high gradient patches which include rich texture details and strong edge information, the classical low rank method always destroys the critical image details and fails to preserve the edge structures. To tackle the above mentioned problems, in this paper, we propose a new multi-layer strategy and reconstruction model which combines the low rank and local rank regularization for image super resolution. In the proposed method, firstly, considering that each layer of the decomposed image shares the similar edge or texture information, which contributes to searching enough and more accurate similar patches to faithfully characterize each exemplar patch, we can decompose the input image into multi-layer images to resolve the problem of destroying critical image details. Next, we incorporate the local rank regularization into the low rank model to develop a new reconstruction model to effectively reconstruct the image details and sharpen the edge structures simultaneously. The experimental results on various images validate the superiority of the proposed method in terms of visual quality and objective indices.