LUMINA-Net通过多阶段光照与降噪模块，提升暗光图像质量。
LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement
- 分阶段处理光照与反射，自适应增强暗光图像亮度。
- 在LOL和SICE数据集上优于现有方法，PSNR/SSIM/LPIPS均提升。
- 适合需要真实感增强的暗光图像处理场景。
低光照图像增强（LLIE）是计算机视觉中提升弱光环境下图像视觉质量的关键任务。传统方法常面临噪声、过曝和色彩失真问题，导致图像质量显著下降。为此，我们提出LUMINA-Net，一种无需监督的深度学习框架，通过整合多阶段光照与反射模块，从低光照图像对中学习自适应先验。为辅助Retinex分解，采用简单自监督机制去除原始图像中的不当特征。首先，光照模块智能调节亮度与对比度，同时保留细微纹理；其次，反射模块引入空间注意力与通道特征优化机制，有效抑制噪声污染。在LOL和SICE数据集上的大量实验表明，该模型在PSNR、SSIM和LPIPS指标上均超越当前最优方法，验证了其在低光照图像增强中的有效性。
原文摘要 · Abstract (English)
Low-light image enhancement (LLIE) is a crucial task in computer vision aimed at enhancing the visual fidelity of images captured under low-illumination conditions. Conventional methods frequently struggle with noise, overexposure, and color distortion, leading to significant image quality degradation. To address these challenges, we propose LUMINA-Net, an unsupervised deep learning framework that learns adaptive priors from low-light image pairs by integrating multi-stage illumination and reflectance modules. To assist the Retinex decomposition, inappropriate features in the raw image can be removed using a simple self-supervised mechanism. First, the illumination module intelligently adjusts brightness and contrast while 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 extensive experiments on LOL and SICE datasets, evaluated using PSNR, SSIM, and LPIPS metrics, LUMINA-Net surpasses state-of-the-art methods, demonstrating its efficacy in low-light image enhancement.
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