Optimally Weighted Highpass Filters using Multiscale Analysis

Abstract

An obvious approach to image enhancement is to sharpen bright regions of an image more than darker regions. One very simple method to accomplish this is to weight the amount of highpass filtering proportional to the local mean. This gives rise to a class of nonlinear image enhancement filters known as mean-weighted highpass filters. We propose a general framework for studying a class of weighted highpass filters. Our framework, based on a multiscale signal decomposition, allows us to study a wide class of filters and to assess the merits of each. We derive an automatic procedure to optimally tune a filter to the local structure of the image under consideration. The entire algorithm is fully automatic and requires no parameter specification from the user. Several simulations demonstrate the efficacy of the proposed algorithm.