A Novel Progressive Enhancement of Low Light Raw Images

Abstract

Low-light imaging on mobile devices is often difficult due to the issue of not enough light passing through the small aperture, resulting in poor quality images. Most previous work on low-resolution images has focused on a single task, such as illumination, color correction, or noise removal; Noise removal function based on short and long images of the camera model. This technique is less efficient and general in real-world environments that require special camera integration and restoration. In this paper, we propose a lighting system that can integrate illumination variation, color correction, and noise removal to solve this problem. Considering the difficulty of obtaining model-specific data and the maximum content of the resulting image, we created two branches: the coefficient estimation branch and the integration branch. While the computer estimator operates in the sparsely resolved space and estimates the coefficients developed by pairwise learning, the collaborative system operates in the fully resolved space, providing step-by-step integration and denoising. Compared to existing methods, our framework does not need to remember as much information when switching to another camera model, which reduces the effort required to benefit our usage pattern. Through extensive testing, we demonstrate its great potential in real-world low-light applications.