Zero-LED: Zero-Reference Lighting Estimation Diffusion Model for
  Low-Light Image Enhancement
Jinhong He Minglong Xue Zhipu Liu Chengyun Song Senming Zhong
Diffusion model-based low-light image enhancement methods rely heavily on
paired training data, leading to limited extensive application. Meanwhile,
existing unsupervised methods lack effective bridging capabilities for unknown
degradation. To address these limitations, we propose a novel zero-reference
lighting estimation diffusion model for low-light image enhancement called
Zero-LED. It utilizes the stable convergence ability of diffusion models to
bridge the gap between low-light domains and real normal-light domains and
successfully alleviates the dependence on pairwise training data via
zero-reference learning. Specifically, we first design the initial optimization
network to preprocess the input image and implement bidirectional constraints
between the diffusion model and the initial optimization network through
multiple objective functions. Subsequently, the degradation factors of the
real-world scene are optimized iteratively to achieve effective light
enhancement. In addition, we explore a frequency-domain based and semantically
guided appearance reconstruction module that encourages feature alignment of
the recovered image at a fine-grained level and satisfies subjective
expectations. Finally, extensive experiments demonstrate the superiority of our
approach to other state-of-the-art methods and more significant generalization
capabilities. We will open the source code upon acceptance of the paper.