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：基于学习的方法在弱光图像增强领域引起了广泛研究关注，并取得了显著进展。然而，大多数方法仍面临两大问题：高分辨率图像中昂贵的计算成本，以及同步增强与去噪时性能欠佳。为解决这些问题，我们提出BDCE——一种Bootstrap扩散模型，该模型利用曲线参数分布的学习替代正常光照图像本身的学习。具体而言，我们采用曲线估计方法来处理高分辨率图像，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引入去噪模块，以对每次迭代的中间增强结果进行去噪处理。我们在常用基准数据集上评估了BDCE，大量实验表明该方法在定性和定量性能上均达到了最先进水平。