Toward Efficient Deep Blind Raw Image Restoration

Multiple low-vision tasks such as denoising, deblurring and super-resolution depart from a sRGB image and further reduce the degradations, improving the perceptual quality. However, modeling the degradations in the sRGB domain is complicated because of the Image Signal Processor (ISP) transformation. Despite of this known issue, very few methods in the literature work directly with sensor RAW images. In this work we tackle image restoration directly in the RAW domain. We design a new realistic degradation pipeline for training deep blind RAW restoration models. Our pipeline considers realistic sensor noise, motion blur, camera shake, and other common degradations. The models trained with our pipeline and data from multiple sensors, can successfully reduce noise and blur, and recover details in real RAW images captured from different cameras in-the-wild. To the best of our knowledge, this is the most exhaustive analysis on RAW image restoration.