Super-resolution image reconstruction based on guided cost function
Ruo-mei Yan, Yunfeng Zhang, Yunsong Li, Chengke Wu · 2010
Super-resolution reconstruction (SRR) deals with construction of a high-resolution image from a set of blurred, degraded and shifted low-resolution images of a scene. A variety of methods have been proposed to address the SRR problem, nevertheless they are usually based on a simple cost function thus are very sensitive to their assumed model of data and noise, which limits their utility. This paper proposes a novel SRR approach based on Bayesian estimation by minimizing a guided cost function. That is, the intensity variation is incorporated into the similarity term to guide the L1norm minimization. As the adjusted similarity term can identify the outlier in the structure area, the proposed algorithm is structure adaptive and very successful in edge-preserving. Lots of experimental results show that the proposed algorithm has considerable improvement in terms of both objective measurements and visual effects.