  Image signal processors (ISPs) are historically grown legacy software systems
for reconstructing color images from noisy raw sensor measurements. Each
smartphone manufacturer has developed its ISPs with its own characteristic
heuristics for improving the color rendition, for example, skin tones and other
visually essential colors. The recent interest in replacing the historically
grown ISP systems with deep-learned pipelines to match DSLR's image quality
improves structural features in the image. However, these works ignore the
superior color processing based on semantic scene analysis that distinguishes
mobile phone ISPs from DSLRs. Here, we present MetaISP, a single model designed
to learn how to translate between the color and local contrast characteristics
of different devices. MetaISP takes the RAW image from device A as input and
translates it to RGB images that inherit the appearance characteristics of
devices A, B, and C. We achieve this result by employing a lightweight deep
learning technique that conditions its output appearance based on the device of
interest. In this approach, we leverage novel attention mechanisms inspired by
cross-covariance to learn global scene semantics. Additionally, we use the
metadata that typically accompanies RAW images and estimate scene illuminants
when they are unavailable.
