Super-resolution image reconstruction based on guided cost function

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 L₁ norm 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.