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Printed Texture Guided Color Feature Fusion for Impressionism Style Rendering of Oil Paintings

Author

Listed:
  • Jing Geng

    (Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710048, China
    These authors contributed equally to this work.)

  • Li’e Ma

    (Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710048, China
    These authors contributed equally to this work.)

  • Xiaoquan Li

    (National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK)

  • Xin Zhang

    (Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710048, China)

  • Yijun Yan

    (National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK)

Abstract

As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses computer algorithms to render a photo into an artistic painting. Recent work has shown that the ex-traction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed. By fully considering the tonal characteristics and the representative color of the original oil painting, it can fit the tone of the original oil painting image into a stylized image whilst keeping the artist’s creative effect. The experiments have validated the efficacy of the proposed model in comparison to three state-of-the-arts. This method would be more suitable for the works of pointillism painters with a relatively uniform style, especially for natural scenes, otherwise, the results can be less satisfactory.

Suggested Citation

  • Jing Geng & Li’e Ma & Xiaoquan Li & Xin Zhang & Yijun Yan, 2022. "Printed Texture Guided Color Feature Fusion for Impressionism Style Rendering of Oil Paintings," Mathematics, MDPI, vol. 10(19), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3700-:d:937396
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