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.

本文提出了可训练的图像信号处理框架，通过使用智能手机拍摄 RAW 图像，可以生成 DSLR 画质的图像，其中使用了色彩条件 ISP 网络和优化的参数色彩映射，在设计具有高效全局上下文模块的颜色预测网络的同时，使用了鲁棒的遮蔽对齐损失函数。同时推出了 ISP in the Wild（ISPW）数据集，并在两个数据集上取得了新的最先进的结果。