Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustment

Haofeng Huang, Wenhan Yang, Yueyu Hu, Jiaying Liu; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

Enhancement of images captured in low-light conditions remains to be a challenging problem even with the advanced machine learning techniques. The challenges include the ambiguity of the ground truth for a low-light image and the loss of information during the RAW image processing. To tackle the problems, in this paper, we take a novel view to regard low-light image enhancement as an exposure time adjustment problem and propose a corresponding explicit and mathematical definition. Based on that, we construct a RAW-Guiding exposure time adjustment Network (RGNET), which overcomes RGB images' nonlinearity and RAW images' inaccessibility. That is, RGNET is only trained with RGB images and corresponding RAW images, which helps project nonlinear RGB images into a linear domain, simultaneously without using RAW images in the testing phase. Furthermore, our network consists of three individual sub-modules for unprocessing, reconstruction and processing, respectively. To the best of our knowledge, the proposed sub-net for unprocessing is the first learning-based unprocessing method. After the joint training of three parts, each pre-trained seperately with the RAW image guidance, experimental results demonstrate that RGNET outperforms state-of-the-art low-light image enhancement methods.