Image harmonization is a critical task in computer vision, which aims to
adjust the fore-ground to make it compatible with the back-ground. Recent works
mainly focus on using global transformation (i.e., normalization and color
curve rendering) to achieve visual consistency. However, these model ignore
local consistency and their model size limit their harmonization ability on
edge devices. Inspired by the dynamic deep networks that adapt the model
structures or parameters conditioned on the inputs, we propose a hierarchical
dynamic network (HDNet) for efficient image harmonization to adapt the model
parameters and features from local to global view for better feature
transformation. Specifically, local dynamics (LD) and mask-aware global
dynamics (MGD) are applied. LD enables features of different channels and
positions to change adaptively and improve the representation ability of
geometric transformation through structural information learning. MGD learns
the representations of fore- and back-ground regions and correlations to global
harmonization. Experiments show that the proposed HDNet reduces more than 80\%
parameters compared with previous methods but still achieves the
state-of-the-art performance on the popular iHarmony4 dataset. Our code is
avaliable in this https URL.

本文提出了一种分层动态网络 (HDNet) 用于图像和谐处理，该网络通过局部动态和面具感知全局动态，从本地到全局视角自适应模型参数和特征改变，以实现更好的特征转换和全局调和。