  Learning RAW-to-sRGB mapping has drawn increasing attention in recent years,
wherein an input raw image is trained to imitate the target sRGB image captured
by another camera. However, the severe color inconsistency makes it very
challenging to generate well-aligned training pairs of input raw and target
sRGB images. While learning with inaccurately aligned supervision is prone to
causing pixel shift and producing blurry results. In this paper, we circumvent
such issue by presenting a joint learning model for image alignment and
RAW-to-sRGB mapping. To diminish the effect of color inconsistency in image
alignment, we introduce to use a global color mapping (GCM) module to generate
an initial sRGB image given the input raw image, which can keep the spatial
location of the pixels unchanged, and the target sRGB image is utilized to
guide GCM for converting the color towards it. Then a pre-trained optical flow
estimation network (e.g., PWC-Net) is deployed to warp the target sRGB image to
align with the GCM output. To alleviate the effect of inaccurately aligned
supervision, the warped target sRGB image is leveraged to learn RAW-to-sRGB
mapping. When training is done, the GCM module and optical flow network can be
detached, thereby bringing no extra computation cost for inference. Experiments
show that our method performs favorably against state-of-the-arts on ZRR and
SR-RAW datasets. With our joint learning model, a light-weight backbone can
achieve better quantitative and qualitative performance on ZRR dataset. Codes
are available at https://github.com/cszhilu1998/RAW-to-sRGB.
