  Given a composite image, image harmonization aims to adjust the foreground to
make it compatible with the background. High-resolution image harmonization is
in high demand, but still remains unexplored. Conventional image harmonization
methods learn global RGB-to-RGB transformation which could effortlessly scale
to high resolution, but ignore diverse local context. Recent deep learning
methods learn the dense pixel-to-pixel transformation which could generate
harmonious outputs, but are highly constrained in low resolution. In this work,
we propose a high-resolution image harmonization network with Collaborative
Dual Transformation (CDTNet) to combine pixel-to-pixel transformation and
RGB-to-RGB transformation coherently in an end-to-end network. Our CDTNet
consists of a low-resolution generator for pixel-to-pixel transformation, a
color mapping module for RGB-to-RGB transformation, and a refinement module to
take advantage of both. Extensive experiments on high-resolution benchmark
dataset and our created high-resolution real composite images demonstrate that
our CDTNet strikes a good balance between efficiency and effectiveness. Our
used datasets can be found in
https://github.com/bcmi/CDTNet-High-Resolution-Image-Harmonization.
