NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression

TL;DR

This paper introduces NDELS, a comprehensive neural network approach that simultaneously enhances nighttime image visibility, reduces low-light issues, and suppresses glare, outperforming existing methods across multiple datasets and metrics.

Contribution

NDELS is the first integrated solution addressing nighttime dehazing, low-light enhancement, and light suppression within a single framework, demonstrating superior performance.

Findings

Outperforms eight state-of-the-art algorithms in quality metrics

Improves color and edge clarity in nighttime images

Validated across four diverse datasets

Abstract

This paper tackles the intricate challenge of improving the quality of nighttime images under hazy and low-light conditions. Overcoming issues including nonuniform illumination glows, texture blurring, glow effects, color distortion, noise disturbance, and overall, low light have proven daunting. Despite the inherent difficulties, this paper introduces a pioneering solution named Nighttime Dehazing, Low-Light Enhancement, and Light Suppression (NDELS). NDELS utilizes a unique network that combines three essential processes to enhance visibility, brighten low-light regions, and effectively suppress glare from bright light sources. In contrast to limited progress in nighttime dehazing, unlike its daytime counterpart, NDELS presents a comprehensive and innovative approach. The efficacy of NDELS is rigorously validated through extensive comparisons with eight state-of-the-art algorithms across four diverse datasets. Experimental results showcase the superior performance of our method, demonstrating its outperformance in terms of overall image quality, including color and edge enhancement. Quantitative (PSNR, SSIM) and qualitative metrics (CLIPIQA, MANIQA, TRES), measure these results.