Abstract
Low-light image enhancement (LLIE) is a crucial task in computer vision aimed to enhance the visual fidelity of images captured under low-illumination conditions. Conventional methods frequently struggle to mitigate pervasive shortcomings such as noise, over-exposure, and color distortion thereby precipitating a pronounced degradation in image quality. To address these challenges, we propose LUMINA-Net an advanced deep learning framework designed specifically by integrating multi-stage illumination and reflectance modules. First, the illumination module intelligently adjusts brightness and contrast levels while meticulously preserving intricate textural details. Second, the reflectance module incorporates a noise reduction mechanism that leverages spatial attention and channel-wise feature refinement to mitigate noise contamination. Through a comprehensive suite of experiments conducted on LOL and SICE datasets using PSNR, SSIM and LPIPS metrics, surpassing state-of-the-art methodologies and showcasing its efficacy in low-light image enhancement.
Abstract (translated)
低光图像增强（LLIE）是计算机视觉中的一个关键任务，旨在提升在低光照条件下捕获的图像的视觉保真度。传统方法经常难以解决诸如噪声、过度曝光和色彩失真的普遍问题，导致图像质量显著下降。为了解决这些挑战，我们提出了LUMINA-Net，这是一种先进的深度学习框架，通过整合多阶段照明模块与反射模块而专门设计。 首先，照明模块智能地调整亮度和对比度水平，同时精细保留复杂的纹理细节。其次，反射模块引入了一种噪声减少机制，该机制利用空间注意力和通道级特征精炼来减轻噪声污染。通过对LOL和SICE数据集进行一系列全面的实验，并使用PSNR、SSIM和LPIPS指标评估，我们的方法超越了现有的最佳方法，展示了在低光图像增强中的有效性。 此段落介绍了LUMINA-Net框架如何通过综合运用照明与反射模块来改进传统LLIE技术，同时强调了其在多项标准测试中表现出的优越性能。
URL
https://arxiv.org/abs/2502.15186
