Progressive Painterly Image Harmonization from Low-level Styles to
  High-level Styles
Li Niu Yan Hong Junyan Cao Liqing Zhang
Painterly image harmonization aims to harmonize a photographic foreground
object on the painterly background. Different from previous auto-encoder based
harmonization networks, we develop a progressive multi-stage harmonization
network, which harmonizes the composite foreground from low-level styles (e.g.,
color, simple texture) to high-level styles (e.g., complex texture). Our
network has better interpretability and harmonization performance. Moreover, we
design an early-exit strategy to automatically decide the proper stage to exit,
which can skip the unnecessary and even harmful late stages. Extensive
experiments on the benchmark dataset demonstrate the effectiveness of our
progressive harmonization network.