A color super-resolution with multiple nonsmooth constraints by hybrid steepest descent method

An efficient scheme is presented to the color super-resolution problem for recovery of a color high-resolution image with knowledge of multiple Bayer filtered low-resolution images. To recover a visually natural high-resolution image, we restrict fairly smooth initial candidates to all images satisfying all bounds imposed on the several nonsmooth convex color total variations as well as a non-smooth convex inter cross correlation measure among color channels. In the proposed scheme, the data-fidelity is optimized in a systematic way, over all initial candidates, with the hybrid steepest descent method for quasi-nonexpansive mappings [Yamada & Ogura 2004], by minimizing successively an weighted average of pure mean square errors between the low-resolution transforms of the high-resolution estimate and the multiple low-resolution images. Numerical examples show that the proposed scheme recovers visually natural high resolution images by resolving the tradeoff between noise suppression and edge preservation of the recovered image while keeping fair inter channel cross correlation among color channels.