Mutually Reinforcing Learning of Decoupled Degradation and Diffusion Enhancement for Unpaired Low-Light Image Lightening

Abstract

Denoising Diffusion Probabilistic Model (DDPM) has demonstrated exceptional performance in low-light enhancement task. However, the dependency on paired training datas has left the generality of DDPM in low-light enhancement largely untapped. Therefore, this paper proposes a mutually reinforcing learning framework of decoupled degradation and diffusion enhancement, named MRLIE, which leverages style guidance from unpaired low-light images to generate pseudo-image pairs that are consistent with the target domain, thereby optimizing the latter diffusion enhancement network in a supervised manner. During the degradation process, the diffusion loss of fixed enhancement network serves as a evaluation metric for structure consistency and is combined with adversarial style loss to form the optimization objective for degradation network. Such loss design ensures that scene structure information is retained during the degradation process. During the enhancement process, the degradation network with frozen parameters continuously generates pseudo-paired low-/normal-light image pairs as training datas, thus the diffusion enhancement network could be progressively optimized. On the whole, the two processes are interdependent and could achieve cooperative improvement in terms of degradation realism and enhancement quality through iterative optimization. Additionally, we propose the Retinex-based decoupled degradation strategy for simulating the complex degradation in real low-light imaging, which ensures the color correction and noise suppression capabilities of latter diffusion enhancement network. Extensive experiments show that MRLIE can achieve promising results and better generality across various datasets.