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Laser & Optoelectronics Progress, Volume. 56, Issue 13, 131001(2019)

Natural Texture Synthesis Algorithm Based on Convolutional Neural Network and Edge Detection

Dingxiang Zhang1 and Yongqian Tan2、*
Author Affiliations
  • 1 Department of Electronic Information Engineering, Guizhou Vocational Technology College of Electronics & Information, Kaili, Guizhou 556000, China;
  • 2 School of Big Data Engineering, Kaili University, Kaili, Guizhou 556011, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (6)
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    Figures & Tables(6)
    VGG overall structure

    Fig. 1. VGG overall structure

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    CNN texture synthesis process

    Fig. 2. CNN texture synthesis process

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    Edge value of arbitrary point

    Fig. 3. Edge value of arbitrary point

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    Edge extraction by using Kirsch operator. (a) Original image; (b) Kirsch edge extraction effect

    Fig. 4. Edge extraction by using Kirsch operator. (a) Original image; (b) Kirsch edge extraction effect

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    Flow chart of improved algorithm

    Fig. 5. Flow chart of improved algorithm

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    Comparison of generated texture images. (a) Input source textures; (b) results of algorithm in Ref. [4]; (c) results of algorithm in Ref. [7]; (d) results of improved algorithm

    Fig. 6. Comparison of generated texture images. (a) Input source textures; (b) results of algorithm in Ref. [4]; (c) results of algorithm in Ref. [7]; (d) results of improved algorithm

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    Dingxiang Zhang, Yongqian Tan. Natural Texture Synthesis Algorithm Based on Convolutional Neural Network and Edge Detection[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131001

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    Paper Information

    Category: Image Processing

    Received: Nov. 13, 2018

    Accepted: Jan. 22, 2019

    Published Online: Jul. 11, 2019

    The Author Email: Tan Yongqian (tanyongqian1@163.com)

    DOI:10.3788/LOP56.131001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology