LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement (2502.15186v1)

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.