ReCo-Diff: Explore Retinex-Based Condition Strategy in Diffusion Model
  for Low-Light Image Enhancement
Yuhui Wu Guoqing Wang Zhiwen Wang Yang Yang Tianyu Li Peng Wang Chongyi Li Heng Tao Shen
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.