Retinex-based low-light image enhancement with multi-channel feature optimization

Low-light conditions reduce brightness and contrast, obscure structural details, and amplify noise, which degrades visual quality and adversely affects downstream tasks, such as object detection and segmentation. Despite significant advances in deep learning-based enhancement, existing approaches still struggle to simultaneously balance brightness, detail preservation, and color fidelity. To address this, we propose a two-stage Retinex-based algorithm guided by multi-channel integrated feature optimization. Our main contributions are threefold. (1) We introduce a three-channel illumination decomposition strategy that models RGB illumination independently to mitigate color distortion. (2) We design a U-Net-based decomposition network with deformable convolutions, dual-layer attention, and selective-kernel fusion for multi-scale feature extraction. (3) We develop a two-branch fusion network incorporating detail enhancement, low-frequency filtering, and a curve-based illumination adjustment module for artifact-free enhancement. Extensive experiments on standard datasets show that our method outperforms state-of-the-art algorithms in terms of PSNR, SSIM, and NIQE. Furthermore, experiments on the ExDark dataset demonstrate that the proposed method improves object detection mAP to 0.193, representing a 67.8% increase over the original low-light images, thereby validating its effectiveness for downstream vision tasks.