Unprocessed sensor outputs (RAW images) potentially improve both low-level and high-level computer vision algorithms, but the lack of large-scale RAW image datasets is a barrier to research. Thus, reversed Image Signal Processing (ISP) which converts existing RGB images into RAW images has been studied. However, most existing methods require camera-specific metadata or paired RGB and RAW images to model the conversion, and they are not always available. In addition, there are issues in handling diverse ISPs and recovering global illumination. To tackle these limitations, we propose a self-supervised reversed ISP method that does not require metadata and paired images. The proposed method converts a RGB image into a RAW-like image taken in the same environment with the same sensor as a reference RAW image by dynamically selecting parameters of the reversed ISP pipeline based on the reference RAW image. The parameter selection is trained via pseudo paired data created from unpaired RGB and RAW images. We show that the proposed method is able to learn various reversed ISPs with comparable accuracy to other state-of-the-art supervised methods and convert unknown RGB images from COCO and Flickr1M to target RAW-like images more accurately in terms of pixel distribution. We also demonstrate that our generated RAW images improve performance on real RAW image object detection task.

翻译：未处理的传感器输出（RAW图像）有望提升低层和高层计算机视觉算法的性能，但大规模RAW图像数据集的缺乏阻碍了相关研究。为此，逆向图像信号处理（ISP）——将现有RGB图像转换为RAW图像的方法——已受到关注。然而，现有方法大多依赖相机特定元数据或成对的RGB与RAW图像来建模转换过程，而这类数据并非始终可得。此外，处理多样化的ISP流程和恢复全局光照仍面临挑战。为解决这些局限，我们提出一种无需元数据和成对图像的自监督逆向ISP方法。该方法以参考RAW图像为依据，通过动态选择逆向ISP流水线的参数，将RGB图像转换为与参考RAW图像在同一环境、相同传感器下拍摄的类RAW图像。参数选择通过从未成对的RGB与RAW图像生成的伪成对数据进行训练。实验表明，该方法能够学习多种逆向ISP流程，其精度与现有监督方法相当，并能更准确地（基于像素分布）将来自COCO和Flickr1M的未知RGB图像转换为目标类RAW图像。我们还证明，生成的RAW图像能提升真实RAW图像目标检测任务的性能。