  A common problem for composite images is the incompatibility of their
foreground and background components. Image harmonization aims to solve this
problem, making the whole image look more authentic and coherent. Most existing
solutions predict lookup tables (LUTs) or reconstruct images, utilizing various
attributes of composite images. Recent approaches have primarily focused on
employing global transformations like normalization and color curve rendering
to achieve visual consistency, and they often overlook the importance of local
visual coherence. We present a patch-based harmonization network consisting of
novel Patch-based normalization (PN) blocks and a feature extractor based on
statistical color transfer. Extensive experiments demonstrate the network's
high generalization capability for different domains. Our network achieves
state-of-the-art results on the iHarmony4 dataset. Also, we created a new human
portrait harmonization dataset based on FFHQ and checked the proposed method to
show the generalization ability by achieving the best metrics on it. The
benchmark experiments confirm that the suggested patch-based normalization
block and feature extractor effectively improve the network's capability to
harmonize portraits. Our code and model baselines are publicly available.
