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Hierarchical Dynamic Image Harmonization by Chen, Haoxing; Gu, Zhangxuan; Li, Yaohui; Lan, Jun; Meng, Changhua; Wang, Weiqiang; Li, Huaxiong is a scholarly article available to read on EtoBox.

What is Hierarchical Dynamic Image Harmonization about?

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 for

Author

Chen, Haoxing; Gu, Zhangxuan; Li, Yaohui; Lan, Jun; Meng, Changhua; Wang, Weiqiang; Li, Huaxiong

Published

2022

Language

EN