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.

翻译：基于学习的方法在低光照图像增强领域吸引了大量研究关注，并取得了显著进展。然而，大多数方法仍面临两个主要问题：高分辨率图像的计算成本高昂，以及同时增强与去噪的性能不理想。为解决这些问题，我们提出BDCE——一种利用曲线参数分布学习（而非正常光照图像本身）的Bootstrap扩散模型。具体而言，我们采用曲线估计方法处理高分辨率图像，其中曲线参数由我们的Bootstrap扩散模型进行估计。此外，在曲线调整的每次迭代中应用去噪模块，以消除每次迭代中中间增强结果的噪声。我们在常用基准数据集上评估BDCE，大量实验表明，该方法在定性和定量性能上均达到最先进水平。