Learning-based methods have attracted a lot of research attention and led to
significant improvements in low-light image enhancement. However, most of them
still suffer from two main problems: expensive computational cost in high
resolution images and unsatisfactory performance in simultaneous enhancement
and denoising. To address these problems, we propose BDCE, a bootstrap
diffusion model that exploits the learning of the distribution of the curve
parameters instead of the normal-light image itself. Specifically, we adopt the
curve estimation method to handle the high-resolution images, where the curve
parameters are estimated by our bootstrap diffusion model. In addition, a
denoise module is applied in each iteration of curve adjustment to denoise the
intermediate enhanced result of each iteration. We evaluate BDCE on commonly
used benchmark datasets, and extensive experiments show that it achieves
state-of-the-art qualitative and quantitative performance.

使用 BDCE，一种基于学习的曲线参数分布估计模型，来解决高分辨率图像的计算成本高和同时增强和去噪效果不佳的问题，并通过大量实验证明其在常用基准数据集上达到了最先进的定性和定量性能。