LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models

• URL: http://arxiv.org/abs/2407.08939v1
• Date: Fri, 12 Jul 2024 02:54:43 GMT
• Title: LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models
• Authors: Hai Jiang, Ao Luo, Xiaohong Liu, Songchen Han, Shuaicheng Liu,
• Abstract summary: We propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement. Experiments on publicly available real-world benchmarks show that the proposed LightenDiffusion outperforms state-of-the-art unsupervised competitors.
• Score: 39.28266945709169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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