LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models
<|reference_start|>LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models: In this paper, we propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement, named LightenDiffusion. Specifically, we present a content-transfer decomposition network that performs Retinex decomposition within the latent space instead of image space as in previous approaches, enabling the encoded features of unpaired low-light and normal-light images to be decomposed into content-rich reflectance maps and content-free illumination maps. Subsequently, the reflectance map of the low-light image and the illumination map of the normal-light image are taken as input to the diffusion model for unsupervised restoration with the guidance of the low-light feature, where a self-constrained consistency loss is further proposed to eliminate the interference of normal-light content on the restored results to improve overall visual quality. Extensive experiments on publicly available real-world benchmarks show that the proposed LightenDiffusion outperforms state-of-the-art unsupervised competitors and is comparable to supervised methods while being more generalizable to various scenes. Our code is available at https://github.com/JianghaiSCU/LightenDiffusion.<|reference_end|>
@article{jiang2024lightendiffusion:,
    title={LightenDiffusion: Unsupervised Low-Light Image Enhancement with
  Latent-Retinex Diffusion Models},
    author={Hai Jiang, Ao Luo, Xiaohong Liu, Songchen Han, Shuaicheng Liu},
    journal={arXiv preprint arXiv:2407.08939},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08939},
    primaryClass={cs.CV}
}