论文标题
反向图像信号处理和原始重建。目标2022挑战报告
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
论文作者
论文摘要
相机使用集成图像信号处理器（ISP）捕获传感器原始图像，并将其转换为适合人眼的宜人的RGB图像。由于其与场景辐照度的线性关系，12bits的大量信息以及传感器设计，许多低级视觉任务在原始领域（例如图像denoising，白平衡）都运行。尽管如此，与已经大型和公共RGB数据集相比，原始图像数据集稀缺，收集更昂贵。 本文介绍了针对反向图像信号处理和原始重建的目标2022挑战。我们旨在从没有元数据的相应RGB中恢复原始传感器图像，然后这样做“反向” ISP变换。提出的方法和基准为这个低级视力逆问题建立了最新的方法，并且生成现实的原始传感器读取可能会使其他任务有益于其他任务，例如denoising和super-slosolution。
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the RAW domain (e.g. image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public RGB datasets. This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation. The proposed methods and benchmark establish the state-of-the-art for this low-level vision inverse problem, and generating realistic raw sensor readings can potentially benefit other tasks such as denoising and super-resolution.
