  Image harmonization aims to achieve visual consistency in composite images by
adapting a foreground to make it compatible with a background. However,
existing methods always only use the real image as the positive sample to guide
the training, and at most introduce the corresponding composite image as a
single negative sample for an auxiliary constraint, which leads to limited
distortion knowledge, and further causes a too large solution space, making the
generated harmonized image distorted. Besides, none of them jointly constrain
from the foreground self-style and foreground-background style consistency,
which exacerbates this problem. Moreover, recent region-aware adaptive instance
normalization achieves great success but only considers the global background
feature distribution, making the aligned foreground feature distribution
biased. To address these issues, we propose a self-consistent style contrastive
learning scheme (SCS-Co). By dynamically generating multiple negative samples,
our SCS-Co can learn more distortion knowledge and well regularize the
generated harmonized image in the style representation space from two aspects
of the foreground self-style and foreground-background style consistency,
leading to a more photorealistic visual result. In addition, we propose a
background-attentional adaptive instance normalization (BAIN) to achieve an
attention-weighted background feature distribution according to the
foreground-background feature similarity. Experiments demonstrate the
superiority of our method over other state-of-the-art methods in both
quantitative comparison and visual analysis.
