The Visual Computer

, Volume 32, Issue 1, pp 43–55 | Cite as

Feature-aware natural texture synthesis

  • Fuzhang Wu
  • Weiming DongEmail author
  • Yan Kong
  • Xing Mei
  • Dong-Ming Yan
  • Xiaopeng Zhang
  • Jean-Claude Paul
Original Article


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.


Texture synthesis Texture feature analysis Composite texture Feature-aware synthesis 



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.

Supplementary material

371_2014_1054_MOESM1_ESM.pdf (26.2 mb)
Supplementary material 1 (pdf 26806 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Fuzhang Wu
    • 1
  • Weiming Dong
    • 1
    Email author
  • Yan Kong
    • 1
  • Xing Mei
    • 1
  • Dong-Ming Yan
    • 1
    • 2
  • Xiaopeng Zhang
    • 1
  • Jean-Claude Paul
    • 3
  1. 1.LIAMA-NLPRInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.Visual Computing Center, King Abdullah University of Science and TechnologyThuwalKingdom of Saudi Arabia
  3. 3.Project CAD, INRIAParisFrance

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