Reflectance-Guided, Contrast-Accumulated Histogram Equalization

TL;DR

This paper introduces a novel histogram equalization technique that adaptively enhances image contrast by incorporating reflectance-based spatial information, effectively improving detail visibility without sacrificing global contrast.

Contribution

It presents a reflectance-guided, contrast-accumulated histogram equalization method that adaptively enhances local and global contrast using spatial information from reflectance estimation.

Findings

Improves local and global contrast simultaneously.

Effectively reveals details in dark regions.

Maintains overall image brightness and contrast.

Abstract

Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem, we propose a histogram equalization-based method that adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. This method incorporates the spatial information provided by image context in density estimation for discriminative histogram equalization. To minimize the adverse effect of non-uniform illumination, we propose defining spatial information on the basis of image reflectance estimated with edge preserving smoothing. Our method works particularly well for determining how the background brightness should be adaptively adjusted and for revealing useful image details hidden in the dark.