Image sharpening using permutation weighted medians

A class of robust weighted median (WM) sharpening algorithms is developed in this paper. Unlike traditional linear sharpening methods, weighted median sharpeners are shown to be less sensitive to noise or to image artifacts introduced by compression algorithms. The WM sharpening structure is extended to include data dependent weights under the framework of permutation weighted medians leading to sharpeners that have tunable robustness properties. A statistical analysis of the various algorithms is presented. Experiments illustrate the sharpening of images with compression artifacts and of images with background film-grain noise.