An iterated randomized search algorithm for large-scale texture synthesis and manipulations
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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.
KeywordsTexture synthesis Coherent propagation Random search Texture transfer Approximate match patch
Thanks are given to my friends, Mr.Yuanzhang Chang, Mr.Gang Sun and Mr. Xiaosheng Li for their helps and discussions. Thanks also to Professor Li-Yi Wei and Dr.Vivek Kwatra for sharing their texture datasets via their respective web sites. The research has been supported by NSFC Grant (61202154) and the research grant of University of Macau. And last but not the least, thanks are given to my families.
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