Over-enhancement Reduction in Local Histogram Equalization using its Degrees of Freedom

TL;DR

This paper addresses over-enhancement in local histogram equalization by exploring the solution space through degrees of freedom, selecting solutions that maximize similarity to the original image while maintaining local contrast.

Contribution

It introduces a method to reduce over-enhancement by leveraging the degrees of freedom in LHE, optimizing for PSNR or SSIM to preserve image fidelity.

Findings

Reduced over-enhancement in LHE images

Achieved higher PSNR and SSIM scores

Maintained local contrast effectively

Abstract

A well-known issue of local (adaptive) histogram equalization (LHE) is over-enhancement (i.e., generation of spurious details) in homogenous areas of the image. In this paper, we show that the LHE problem has many solutions due to the ambiguity in ranking pixels with the same intensity. The LHE solution space can be searched for the images having the maximum PSNR or structural similarity (SSIM) with the input image. As compared to the results of the prior art, these solutions are more similar to the input image while offering the same local contrast. Index Terms: histogram modification or specification, contrast enhancement