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.

翻译：低光照图像增强是计算机视觉领域的一项关键任务，旨在提升在低光照条件下捕获图像的视觉保真度。传统方法通常难以有效缓解噪声、过曝和色彩失真等普遍存在的缺陷，从而导致图像质量显著下降。为应对这些挑战，我们提出了LUMINA-Net——一种通过集成多阶段光照与反射率模块专门设计的先进深度学习框架。首先，光照模块能智能调节亮度与对比度，同时精细保留复杂的纹理细节。其次，反射率模块结合了降噪机制，该机制利用空间注意力与通道级特征优化来减轻噪声污染。通过在LOL和SICE数据集上使用PSNR、SSIM及LPIPS指标进行的全面实验，本方法超越了现有先进技术，展现了其在低光照图像增强中的卓越效能。