A New Algorithmic Approach for Contrast Enhancement

A novel algorithmic approach for optimal contrast enhancement is proposed. A measure of expected contrast and a sister measure of tone subtlety are defined for gray level transform functions. These definitions allow us to depart from the current practice of histogram equalization and formulate contrast enhancement as a problem of maximizing the expected contrast measure subject to a limit on tone distortion and possibly other constraints that suppress artifacts. The resulting contrast-tone optimization problem can be solved efficiently by linear programming. The proposed constrained optimization framework for contrast enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new technique over histogram equalization.