  Recent works on image harmonization solve the problem as a pixel-wise image
translation task via large autoencoders. They have unsatisfactory performances
and slow inference speeds when dealing with high-resolution images. In this
work, we observe that adjusting the input arguments of basic image filters,
e.g., brightness and contrast, is sufficient for humans to produce realistic
images from the composite ones. Hence, we frame image harmonization as an
image-level regression problem to learn the arguments of the filters that
humans use for the task. We present a Harmonizer framework for image
harmonization. Unlike prior methods that are based on black-box autoencoders,
Harmonizer contains a neural network for filter argument prediction and several
white-box filters (based on the predicted arguments) for image harmonization.
We also introduce a cascade regressor and a dynamic loss strategy for
Harmonizer to learn filter arguments more stably and precisely. Since our
network only outputs image-level arguments and the filters we used are
efficient, Harmonizer is much lighter and faster than existing methods.
Comprehensive experiments demonstrate that Harmonizer surpasses existing
methods notably, especially with high-resolution inputs. Finally, we apply
Harmonizer to video harmonization, which achieves consistent results across
frames and 56 fps at 1080P resolution. Code and models are available at:
https://github.com/ZHKKKe/Harmonizer.
