  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.
