ReCo-Diff: Explore Retinex-Based Condition Strategy in Diffusion Model for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2312.12826v1
• Date: Wed, 20 Dec 2023 08:05:57 GMT
• Title: ReCo-Diff: Explore Retinex-Based Condition Strategy in Diffusion Model for Low-Light Image Enhancement
• Authors: Yuhui Wu, Guoqing Wang, Zhiwen Wang, Yang Yang, Tianyu Li, Peng Wang, Chongyi Li, Heng Tao Shen
• Abstract summary: Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. We propose ReCo-Diff, a novel approach that incorporates Retinex-based prior as an additional pre-processing condition.
• Score: 70.10216029444543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. In this study, we propose ReCo-Diff, a novel approach that incorporates Retinex-based prior as an additional pre-processing condition to regulate the generating capabilities of the diffusion model. ReCo-Diff first leverages a pre-trained decomposition network to produce initial reflectance and illumination maps of the low-light image. Then, an adjustment network is introduced to suppress the noise in the reflectance map and brighten the illumination map, thus forming the learned Retinex-based condition. The condition is integrated into a refinement network, implementing Retinex-based conditional modules that offer sufficient guidance at both feature- and image-levels. By treating Retinex theory as a condition, ReCo-Diff presents a unique perspective for establishing an LLIE-specific diffusion model. Extensive experiments validate the rationality and superiority of our ReCo-Diff approach. The code will be made publicly available.

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