High performance super-resolution reconstruction of multi-frame degraded images with local weighted anisotropy and successive regularization

Abstract

Multi-frame image super-resolution (SR) is a procedure which takes several observed degraded low-resolution (LR) images, and processes them together to synthesize one or more high-quality high-resolution (HR) images. Due to an insufficient number of LR images and ill-conditioned blur operators, SR image reconstruction is a typical ill-posed inverse problem. However, the regularization approach can be used as an effective means to solve this kind of problems. In this paper, we propose an algorithm for multi-frame SR reconstruction with two new types of regularization terms, termed as local weighted anisotropy regularization and successive regularization toward iteration process, which are characterized by strong ability of suppressing noise and preserving edge information in HR image reconstruction. Furthermore, an iterative refinement procedure obtained superior result by employing Bregman iteration. Experiment results show that the effectiveness of the proposed regularization method and corresponding algorithm for SR reconstruction.