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An Improved Style Transfer Algorithm Using Feedforward Neural Network for Real-Time Image Conversion

Author

Listed:
  • Chang Zhou

    (College of Information Engineering, Yangzhou University, Yangzhou 225000, China)

  • Zhenghong Gu

    (College of Information Engineering, Yangzhou University, Yangzhou 225000, China)

  • Yu Gao

    (College of Information Engineering, Yangzhou University, Yangzhou 225000, China)

  • Jin Wang

    (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410000, China
    School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350000, China)

Abstract

Creation of art is a complex process for its abstraction and novelty. In order to create those art with less cost, style transfer using advanced machine learning technology becomes a popular method in computer vision field. However, traditional transferred image still troubles with color anamorphosis, content losing, and time-consuming problems. In this paper, we propose an improved style transfer algorithm using the feedforward neural network. The whole network is composed of two parts, a style transfer network and a loss network. The style transfer network owns the ability of directly mapping the content image into the stylized image after training. Content loss, style loss, and Total Variation (TV) loss are calculated by the loss network to update the weight of the style transfer network. Additionally, a cross training strategy is proposed to better preserve the details of the content image. Plenty of experiments are conducted to show the superior performance of our presented algorithm compared to the classic neural style transfer algorithm.

Suggested Citation

  • Chang Zhou & Zhenghong Gu & Yu Gao & Jin Wang, 2019. "An Improved Style Transfer Algorithm Using Feedforward Neural Network for Real-Time Image Conversion," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5673-:d:276415
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    References listed on IDEAS

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    1. Jin Wang & Yu Gao & Wei Liu & Arun Kumar Sangaiah & Hye-Jin Kim, 2019. "An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    2. Lingping Kong & Jeng-Shyang Pan & Václav Snášel & Pei-Wei Tsai & Tien-Wen Sung, 2018. "An energy-aware routing protocol for wireless sensor network based on genetic algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(3), pages 451-463, March.
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    Cited by:

    1. Muhammad Rashid & Muhammad Attique Khan & Majed Alhaisoni & Shui-Hua Wang & Syed Rameez Naqvi & Amjad Rehman & Tanzila Saba, 2020. "A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection," Sustainability, MDPI, vol. 12(12), pages 1-21, June.

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