Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement
<|reference_start|>Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement: 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.<|reference_end|>
@article{chen2024retinex-rawmamba:,
    title={Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW
  Image Enhancement},
    author={Xianmin Chen, Peiliang Huang, Xiaoxu Feng, Dingwen Zhang, Longfei Han,
  Junwei Han},
    journal={arXiv preprint arXiv:2409.07040},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07040},
    primaryClass={cs.CV eess.IV}
}