  Modern end-to-end image signal processors (ISPs) can learn complex mappings
from RAW/XYZ data to sRGB (or inverse), opening new possibilities in image
processing. However, as the diversity of camera models continues to expand,
developing and maintaining individual ISPs is not sustainable in the long term,
which inherently lacks versatility, hindering the adaptability to multiple
camera models. In this paper, we propose a novel pipeline, Uni-ISP, which
unifies the learning of ISPs from multiple cameras, offering an accurate and
versatile processor to multiple camera models. The core of Uni-ISP is
leveraging device-aware embeddings through learning inverse/forward ISPs and
its special training scheme. By doing so, Uni-ISP not only improves the
performance of inverse/forward ISPs but also unlocks a variety of new
applications inaccessible to existing learned ISPs. Moreover, since there is no
dataset synchronously captured by multiple cameras for training, we construct a
real-world 4K dataset, FiveCam, comprising more than 2,400 pairs of sRGB-RAW
images synchronously captured by five smartphones. We conducted extensive
experiments demonstrating Uni-ISP's accuracy in inverse/forward ISPs (with
improvements of +1.5dB/2.4dB PSNR), its versatility in enabling new
applications, and its adaptability to new camera models.
