LL-Diff: Low-Light Image Enhancement Utilizing Langevin Sampling Diffusion.

In this paper, we propose a new algorithm called LL-Diff, which is innovative compared to traditional augmentation methods in that it introduces the sampling method of Langevin dynamics. This sampling approach simulates the motion of particles in complex environments and can better handle noise and details in low-light conditions. We also incorporate a causal attention mechanism to achieve causality and address the issue of confounding effects. This attention mechanism enables us to better capture local information while avoiding over-enhancement. We have conducted experiments on the LOL-V1 and LOL-V2 datasets, and the results show that LL-Diff significantly improves computational speed and several evaluation metrics, demonstrating the superiority and effectiveness of our method for low-light image enhancement tasks. The code will be released on GitHub when the paper has been accepted. [ABSTRACT FROM AUTHOR]