CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks.

Cameras currently allow access to two image states: (i) a minimally processed linear raw-RGB image state or (ii) a highly-processed nonlinear image state (i.e., sRGB). There are many computer vision tasks that work best with a linear image state. A number of methods have been proposed to "unprocess'' nonlinear images back to a raw-RGB state. However, existing methods have a drawback because raw-RGB images are sensor-specific. As a result, it is necessary to know which camera produced the sRGB output and use a method or network tailored for that sensor to properly unprocess it. This paper addresses this limitation by exploiting another camera image state that is not available as an output, but it is available inside the camera pipeline. In particular, cameras apply a colorimetric conversion step to convert the raw-RGB image to a device-independent space based on the CIE XYZ color space before they apply the nonlinear photo-finishing. Leveraging this canonical state, we propose a deep learning framework that can unprocess a nonlinear image back to the canonical CIE XYZ image. This image can then be processed by any low-level computer vision operator. We demonstrate the usefulness of our framework on several vision tasks and show significant improvements.