Adaptive MAP high-resolution image reconstruction algorithm using local statistics

Abstract

In this paper, we propose an adaptive MAP (Maximum A Posteriori) high-resolution image reconstruction algorithm using local. statistics. In order to preserve an edge information of an original high-resolution image, a visibility function defined by local statistics of the low-resolution image is incorporated into MAP estimation process, so that the local smoothness is adaptively controlled. The weighted non-quadratic convex functional is defined to obtain the optimal solution that is as close as possible to the original high-resolution image. An iterative algorithm is utilized for obtaining the solution. The smoothing parameter is updated at each iteration step from the partially reconstructed high-resolution image, and therfore no knowledge about of the original high-resolution image is required. Experimental results demonstrate the capability of the proposed algorithm.