直接在RAW域修复图像，提升去噪与去模糊效果。
Toward Efficient Deep Blind RAW Image Restoration
- 构建真实传感器噪声、运动模糊等退化模拟流程
- 跨多相机传感器训练，有效恢复细节并降噪
- 首个全面分析RAW图像修复的系统性工作
许多低视觉任务如去噪、去模糊和超分辨率通常在RGB图像上进行，进一步减轻退化以提升质量。然而，在sRGB域建模退化过程因图像信号处理器（ISP）变换而变得复杂。尽管存在这一已知问题，现有文献中极少方法直接处理传感器原始RAW图像。本文直接在RAW域开展图像修复研究，设计了一种新的、真实的退化生成流程，用于训练深度盲反卷积模型。该流程综合考虑了真实传感器噪声、运动模糊、相机抖动及其他常见退化因素。使用该流程生成的数据及多传感器数据训练的模型，可有效降低噪声与模糊，并恢复不同相机拍摄的RAW图像中的细节。据我们所知，这是对RAW图像修复最全面的分析。代码已公开于 https://github.com/mv-lab/AISP。
原文摘要 · Abstract (English)
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
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