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Feature-aware natural texture synthesis

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Abstract

This article presents a framework for natural texture synthesis and processing. This framework is motivated by the observation that given examples captured in natural scene, texture synthesis addresses a critical problem, namely, that synthesis quality can be affected adversely if the texture elements in an example display spatially varied patterns, such as perspective distortion, the composition of different sub-textures, and variations in global color pattern as a result of complex illumination. This issue is common in natural textures and is a fundamental challenge for previously developed methods. Thus, we address it from a feature point of view and propose a feature-aware approach to synthesize natural textures. The synthesis process is guided by a feature map that represents the visual characteristics of the input texture. Moreover, we present a novel adaptive initialization algorithm that can effectively avoid the repeat and verbatim copying artifacts. Our approach improves texture synthesis in many images that cannot be handled effectively with traditional technologies.

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Acknowledgments

We thank anonymous reviewers for their valuable input. We thank Chongyang Ma for providing some results and valuable comments in the preparation of this paper. This work is supported by National Natural Science Foundation of China under project Nos. 61172104, 61271430, 61201402, 61372184, 61372168, and 61331018.

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Authors and Affiliations

  1. LIAMA-NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Fuzhang Wu, Weiming Dong, Yan Kong, Xing Mei, Dong-Ming Yan & Xiaopeng Zhang

  2. Visual Computing Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia

    Dong-Ming Yan

  3. Project CAD, INRIA, Paris, France

    Jean-Claude Paul

Authors
  1. Fuzhang Wu
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  2. Weiming Dong
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  3. Yan Kong
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  4. Xing Mei
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  5. Dong-Ming Yan
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  6. Xiaopeng Zhang
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  7. Jean-Claude Paul
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Correspondence to Weiming Dong.

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Wu, F., Dong, W., Kong, Y. et al. Feature-aware natural texture synthesis. Vis Comput 32, 43–55 (2016). https://doi.org/10.1007/s00371-014-1054-y

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