Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement

• Published in: arXiv.org
• Main Authors: Chen, Xianmin, Huang, Peiliang, Feng, Xiaoxu, Zhang, Dingwen, Han, Longfei, Han, Junwei
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 13.12.2024
• Subjects: Color; Decomposition; Illumination; Image contrast; Image enhancement; Image filters; Mapping; Neural networks; Noise reduction; Performance evaluation; State-of-the-art reviews; Task complexity
• ISSN: 2331-8422

Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. Many deep learning-based methods have been developed to address this issue and have shown promising results in recent years. However, single-stage methods, which attempt to unify the complex mapping across both domains, leading to limited denoising performance. In contrast, two-stage approaches typically decompose a raw image with color filter arrays (CFA) into a four-channel RGGB format before feeding it into a neural network. However, this strategy overlooks the critical role of demosaicing within the Image Signal Processing (ISP) pipeline, leading to color distortions under varying lighting conditions, especially in low-light scenarios. To address these issues, we design a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs. Furthermore, we present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction. By bridging demosaicing and denoising, better raw image enhancement is achieved. Experimental evaluations conducted on public datasets SID and MCR demonstrate that our proposed RAWMamba achieves state-of-the-art performance on cross-domain mapping.