  Image harmonization is a critical task in computer vision, which aims to
adjust the foreground to make it compatible with the background. Recent works
mainly focus on using global transformations (i.e., normalization and color
curve rendering) to achieve visual consistency. However, these models ignore
local visual consistency and their huge model sizes limit their harmonization
ability on edge devices. In this paper, we propose a hierarchical dynamic
network (HDNet) to adapt features from local to global view for better feature
transformation in efficient image harmonization. Inspired by the success of
various dynamic models, local dynamic (LD) module and mask-aware global dynamic
(MGD) module are proposed in this paper. Specifically, LD matches local
representations between the foreground and background regions based on semantic
similarities, then adaptively adjust every foreground local representation
according to the appearance of its $K$-nearest neighbor background regions. In
this way, LD can produce more realistic images at a more fine-grained level,
and simultaneously enjoy the characteristic of semantic alignment. The MGD
effectively applies distinct convolution to the foreground and background,
learning the representations of foreground and background regions as well as
their correlations to the global harmonization, facilitating local visual
consistency for the images much more efficiently. Experimental results
demonstrate that the proposed HDNet significantly reduces the total model
parameters by more than 80\% compared to previous methods, while still
attaining state-of-the-art performance on the popular iHarmony4 dataset.
Notably, the HDNet achieves a 4\% improvement in PSNR and a 19\% reduction in
MSE compared to the prior state-of-the-art methods.
