Compress Clean Signal from Noisy Raw Image: A Self-Supervised Approach

Raw images offer unique advantages in many lowlevel visual tasks due to their unprocessed nature. However, this unprocessed state accentuates noise, making raw images challenging to compress effectively. Current compression methods often overlook the ubiquitous noise in raw space, leading to increased bitrates and reduced quality. In this paper, we propose a novel raw image compression scheme that selectively compresses the noise-free component of the input, while discarding its real noise using a self-supervised approach. By excluding noise from the bitstream, both the coding efficiency and reconstruction quality are significantly enhanced. We curate a full-day dataset of raw images with calibrated noise parameters and reference images to evaluate the performance of models under a wide range of input signalnoise ratios. Experimental results demonstrate that our method surpasses existing compression techniques, achieving a more advantageous ratedistortion balance with improvements ranging from +2 to +10dB and yielding a bit saving of 2 to 50 times. The code is available at https: //lizhihao6.github.io/Cleans .