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An Image Denoising Model Based on Nonlinear Partial Diferential Equation Using Deep Learning

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1688))

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Abstract

In this paper, we present a deep neural network-based framework for solving nonlinear partial differential equations (PDEs) and applying in denoising image. A loss function that relies on form PDEs, initial and boundary condition (I/BC) residual was proposed. The proposed loss function is discretization-free and highly parallelizable. The network parameters are determined by using stochastic gradient descent algorithm. We demonstrated the performance of proposed method in solving nonlinear partial diferential equation and applying image denoising. The experimental results from this method were compared to the efficient PDE’s numerical method. We showed that the method attains significant improvements in term image denoising.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam

    Quan Dac Ho

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Quan Dac Ho

  3. Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Quan Dac Ho & Hieu Trung Huynh

Authors
  1. Quan Dac Ho
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  2. Hieu Trung Huynh
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Correspondence to Hieu Trung Huynh .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Seoul, Korea (Republic of)

    Tai M. Chung

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Ho, Q.D., Huynh, H.T. (2022). An Image Denoising Model Based on Nonlinear Partial Diferential Equation Using Deep Learning. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_27

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  • DOI: https://doi.org/10.1007/978-981-19-8069-5_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8068-8

  • Online ISBN: 978-981-19-8069-5

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