A new adaptive iterative regularized algorithm for super-resolution image reconstruction

Abstract

In order to further improve the reconstruction quality of super-resolution image, a new reconstruction algorithm of adaptive super-resolution image sequence is proposed. The algorithm could adaptively estimate the regularization coefficients for each low-resolution image, and self-adaptively update the iterative step size in the iterative process. In the process of super-resolution image reconstruction, the prior information of image are sufficiently utilized, thus the reconstruction effects would be better. In the conventional bilateral total variation (BTV) regularized algorithm, the regularization parameter can be selected adaptively, but the iterative step size is not adaptive. The simulation experiments are performed for Lena image and Fish image, which using the proposed algorithm and the conventional regularized algorithm. And the experimental results indicate that, compared with the conventional adaptive BTV algorithm, this proposed algorithm has a relatively higher peak signal-to-noise ratio (PSNR) and better visual reconstruction effects of image.