  RAW images are rarely shared mainly due to its excessive data size compared
to their sRGB counterparts obtained by camera ISPs. Learning the forward and
inverse processes of camera ISPs has been recently demonstrated, enabling
physically-meaningful RAW-level image processing on input sRGB images. However,
existing learning-based ISP methods fail to handle the large variations in the
ISP processes with respect to camera parameters such as ISO and exposure time,
and have limitations when used for various applications. In this paper, we
propose ParamISP, a learning-based method for forward and inverse conversion
between sRGB and RAW images, that adopts a novel neural-network module to
utilize camera parameters, which is dubbed as ParamNet. Given the camera
parameters provided in the EXIF data, ParamNet converts them into a feature
vector to control the ISP networks. Extensive experiments demonstrate that
ParamISP achieve superior RAW and sRGB reconstruction results compared to
previous methods and it can be effectively used for a variety of applications
such as deblurring dataset synthesis, raw deblurring, HDR reconstruction, and
camera-to-camera transfer.
