Abstract
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 this https URL
Abstract (translated)
多项低视力任务（例如去噪、去模糊和超分辨率）从RGB图像中分离出来，并进一步减少了降解，提高了质量。然而，在sRGB域中建模降解是一个复杂的问题，因为Image Signal Processor（ISP）变换。尽管如此，在文献中很少有直接处理传感器RAW图像的方法。在这项工作中，我们直接在RAW域处理图像修复。我们设计了一个新的真实感降解管道，用于训练深度盲RAW修复模型。我们的管道考虑了真实的传感器噪声、运动模糊、相机振动和其他常见降解。使用我们这个管道训练的模型和来自多个传感器的数据，可以成功减少噪声和模糊，并从不同相机捕捉到的RAW图像中恢复细节。据我们所知，这是关于RAW图像修复的最详尽分析。代码可在此处访问：https://www.xxxxxxx.com/
URL
https://arxiv.org/abs/2409.18204
