Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement
This work addresses computational efficiency and quality issues in low-light image enhancement, which is an incremental improvement for applications like photography or surveillance.
The paper tackles the problems of high computational cost and poor simultaneous enhancement and denoising in high-resolution low-light image enhancement by proposing BDCE, a bootstrap diffusion model that learns curve parameters, achieving state-of-the-art performance on benchmark datasets.
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.