An iterated randomized search algorithm for large-scale texture synthesis and manipulations

CORC > 软件研究所 > 软件所图书馆 > 期刊论文

	An iterated randomized search algorithm for large-scale texture synthesis and manipulations
	Hao, CY ; Chen, YD ; Wu, W ; Wu, EH
刊名	VISUAL COMPUTER
	2015
卷号	31 期号:11 页码:1447-1458
关键词	Texture synthesis Coherent propagation Random search Texture transfer Approximate match patch
ISSN号	0178-2789
中文摘要	In this paper, we introduce a novel iterated random search method for large-scale texture synthesis and manipulations. Previous researches on texture synthesis and manipulation have reached a great achievement both on quality and performance. However, the cost of the popular exhaustive search-based methods is still high especially for large-scale and complex synthesis scenes. Our algorithm contributes great improvements on performances about 2-50 times over the typical patch-based synthesis methods. Texture patterns have been well-known formalized as a Markov Random Field (MRF) whose two hypotheses, stationarity and locality, drive our bold guess that a random sampling may just catch a good match and allows us to propagate the natural coherence in the neighborhood. Meanwhile, the iteration constantly updates the bad guesses to make our algorithm converge fast with the results in the state of the art. We also provide a simple theoretical analysis to compare our iterated randomized search model and the classical synthesis algorithms. Besides, this simple method turns out to work well in various applications as well, such as texture transfer, image completion and video synthesis.
英文摘要	In this paper, we introduce a novel iterated random search method for large-scale texture synthesis and manipulations. Previous researches on texture synthesis and manipulation have reached a great achievement both on quality and performance. However, the cost of the popular exhaustive search-based methods is still high especially for large-scale and complex synthesis scenes. Our algorithm contributes great improvements on performances about 2-50 times over the typical patch-based synthesis methods. Texture patterns have been well-known formalized as a Markov Random Field (MRF) whose two hypotheses, stationarity and locality, drive our bold guess that a random sampling may just catch a good match and allows us to propagate the natural coherence in the neighborhood. Meanwhile, the iteration constantly updates the bad guesses to make our algorithm converge fast with the results in the state of the art. We also provide a simple theoretical analysis to compare our iterated randomized search model and the classical synthesis algorithms. Besides, this simple method turns out to work well in various applications as well, such as texture transfer, image completion and video synthesis.
收录类别	SCI
语种	英语
WOS记录号	WOS:000362680600002
公开日期	2016-12-13
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/17432]
专题	软件研究所_软件所图书馆_期刊论文
推荐引用方式 GB/T 7714	Hao, CY,Chen, YD,Wu, W,et al. An iterated randomized search algorithm for large-scale texture synthesis and manipulations[J]. VISUAL COMPUTER,2015,31(11):1447-1458.
APA	Hao, CY,Chen, YD,Wu, W,&Wu, EH.(2015).An iterated randomized search algorithm for large-scale texture synthesis and manipulations.VISUAL COMPUTER,31(11),1447-1458.
MLA	Hao, CY,et al."An iterated randomized search algorithm for large-scale texture synthesis and manipulations".VISUAL COMPUTER 31.11(2015):1447-1458.