Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions

TL;DR

This paper introduces a novel framework that simultaneously enhances image quality and suppresses noise under complex lighting conditions by combining gradient-domain filtering, Retinex decomposition, and multi-exposure fusion.

Contribution

It proposes a new integrated approach that effectively improves image contrast and reduces noise in challenging illumination scenarios, outperforming existing methods.

Findings

Better contrast enhancement than state-of-the-art methods

Effective noise suppression in complex lighting

Improved image quality on real-world datasets

Abstract

Under challenging light conditions, captured images often suffer from various degradations, leading to a decline in the performance of vision-based applications. Although numerous methods have been proposed to enhance image quality, they either significantly amplify inherent noise or are only effective under specific illumination conditions. To address these issues, we propose a novel framework for simultaneous enhancement and noise suppression under complex illumination conditions. Firstly, a gradient-domain weighted guided filter (GDWGIF) is employed to accurately estimate illumination and improve image quality. Next, the Retinex model is applied to decompose the captured image into separate illumination and reflection layers. These layers undergo parallel processing, with the illumination layer being corrected to optimize lighting conditions and the reflection layer enhanced to improve image quality. Finally, the dynamic range of the image is optimized through multi-exposure fusion and a linear stretching strategy. The proposed method is evaluated on real-world datasets obtained from practical applications. Experimental results demonstrate that our proposed method achieves better performance compared to state-of-the-art methods in both contrast enhancement and noise suppression.