  We propose a trainable Image Signal Processing (ISP) framework that produces
DSLR quality images given RAW images captured by a smartphone. To address the
color misalignments between training image pairs, we employ a color-conditional
ISP network and optimize a novel parametric color mapping between each input
RAW and reference DSLR image. During inference, we predict the target color
image by designing a color prediction network with efficient Global Context
Transformer modules. The latter effectively leverage global information to
learn consistent color and tone mappings. We further propose a robust masked
aligned loss to identify and discard regions with inaccurate motion estimation
during training. Lastly, we introduce the ISP in the Wild (ISPW) dataset,
consisting of weakly paired phone RAW and DSLR sRGB images. We extensively
evaluate our method, setting a new state-of-the-art on two datasets.
