Multiple low-vision tasks such as denoising, deblurring and super-resolution depart from RGB images and further reduce the degradations, improving the quality. However, modeling the degradations in the sRGB domain is complicated because of the Image Signal Processor (ISP) transformations. 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 RAW images captured from different cameras. To the best of our knowledge, this is the most exhaustive analysis on RAW image restoration. Code available at https://github.com/mv-lab/AISP

本文针对图像恢复领域中，直接处理RAW图像的缺乏问题，提出了一种新的现实降解流水线，以训练深度盲RAW恢复模型。研究发现，通过考虑传感器噪声、运动模糊、相机抖动等常见降解，所训练的模型能够有效减少噪声和模糊，恢复不同摄像机捕获的RAW图像中的细节，具有重要的实际应用价值。

高效深度盲RAW图像修复研究