Learned Reverse ISP with Soft Supervision

RAW image serves as the foundation for camera imaging, which resides at the very beginning of the pipeline that generates sRGB images. Unfortunately, owing to special considerations, the information-rich RAW images are forfeited by default in most existing applications. To regain the RAW image, some works attempt to restore RAW images from RGB images. They focus on designing handcrafted model-based methods or complicated networks, however, ignoring the special property of RAW image, i.e., high dynamic range. To make up for this deficiency, we introduce a novel soft supervision, derived from the high dynamic range. Specifically, we propose to soften the original ground-truth as a multivariate Gaussian distribution so that networks could learn much more information. Then, we introduce a soft supervision driven network (SSDNet), based on convolution and transformer, for effectively restoring RAW images from RGB images. Quantitative and qualitative results show the promising restoration performance of RGB-to-RAW. In particular, our method achieved fifth place in the S7 track of AIM Reversed ISP Challenge. The source code will be available at https://github.com/yuezhang98/Learned-Reverse-ISP-with-Soft-Supervision .