When combining very different images which often contain complex objects and backgrounds, producing consistent compositions is a challenging problem requiring seamless image editing. In this paper, we propose a general approach, called object-aware image editing, to obtain consistency in structure, color, and texture in a unified way. Our approach improves upon previous gradient-domain composition in three ways. Firstly, we introduce an iterative optimization algorithm to minimize mismatches on the boundaries when the target region contains multiple objects of interest. Secondly, we propose a mixed-domain consistency metric for measuring gradients and colors, and formulate composition as a unified minimization problem that can be solved with a sparse linear system. In particular, we encode texture consistency using a patch-based approach without searching and matching. Thirdly, we adopt an object-aware approach to separately manipulate the guidance gradient fields for objects of interest and backgrounds of interest, which facilitates a variety of seamless image editing applications. Our unified method outperforms previous state-of-the-art methods in preserving global texture consistency in addition to local structure continuity.
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This work was supported in part by the National Key Research and Development Plan (Grant No. 2016YFC0801005), the National Natural Science Foundation of China (Grant Nos. 61772513 and 61402463), and the Open Foundation Project of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province in China (Grant No. 16kftk01).
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Shiming Ge is an associate professor in the Institute of Information Engineering, Chinese Academy of Sciences. He received his B.S. and Ph.D. degrees from the University of Science and Technology of China. His research mainly focuses on computer vision, deep learning, and AI security. He is a senior member of the IEEE and a member of the ACM.
Xin Jin received his Ph.D. degree in computer science from Beihang University. Currently, he is an assistant professor in Beijing Electronic Science and Technology Institute, China. His research mainly focuses on visual computing and visual media security.
Qiting Ye received his B.S. degree in computer science from Peking University in 2015. He is now a master student in the Institute of Information Engineering, Chinese Academy of Sciences. His major research interests lie in computer vision and deep learning.
Zhao Luo is currently a master candidate in the Institute of Information Engineering, Chinese Academy of Sciences. He received his B.S. degree from the University of Electronic Science and Technology of China. His research interests are object tracking and deep learning.
Qiang Li is currently a professor in Southwest University of Science and Technology. He received his Ph.D. degree from the University of Science and Technology of China. His research mainly focuses on the Internet of things and intelligent information processing.
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Ge, S., Jin, X., Ye, Q. et al. Image editing by object-aware optimal boundary searching and mixed-domain composition. Comp. Visual Media 4, 71–82 (2018). https://doi.org/10.1007/s41095-017-0102-8