Reflectance-Oriented Probabilistic Equalization for Image Enhancement

TL;DR

This paper introduces a novel 2D histogram equalization method that adaptively enhances image brightness and contrast, especially in low-light conditions, by modeling intensity dependencies to improve contrast and reduce noise amplification.

Contribution

It proposes a reflectance-oriented probabilistic equalization technique that incorporates local reflectance differences into histogram estimation for better image enhancement.

Findings

Outperforms existing methods in contrast enhancement.

Effectively improves low-light image brightness without over-enhancement.

Reduces noise amplification during enhancement.

Abstract

Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images. To solve this problem, we propose a novel 2D histogram equalization approach. It assumes intensity occurrence and co-occurrence to be dependent on each other and derives the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). This scheme improves global contrast more effectively and reduces noise amplification. The 2D histogram is defined by incorporating the local pixel value differences in image reflectance into the density estimation to alleviate the adverse effects of dark lighting conditions. Over 500 images were used for evaluation, demonstrating the superiority of our approach over existing studies. It can sufficiently improve the brightness of low-light images while avoiding over-enhancement in normal-light images.