NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression
It addresses the problem of poor image quality in nighttime conditions for applications like surveillance or autonomous driving, presenting a comprehensive but incremental improvement over existing methods.
This paper tackled the challenge of improving nighttime image quality under hazy and low-light conditions by introducing NDELS, a method that enhanced visibility, brightened low-light regions, and suppressed glare, outperforming eight state-of-the-art algorithms across four datasets with superior quantitative and qualitative metrics.
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.