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

被引:0
作者
Ho, Quan Dac [1 ,2 ]
Huynh, Hieu Trung [3 ]
机构
[1] Univ Sci, Fac Math & Comp Sci, Ho Chi Minh City, Vietnam
[2] Vietnam Natl Univ, Ho Chi Minh City, Vietnam
[3] Ind Univ Ho Chi Minh City, Fac Informat Technol, Ho Chi Minh City, Vietnam
来源
FUTURE DATA AND SECURITY ENGINEERING. BIG DATA, SECURITY AND PRIVACY, SMART CITY AND INDUSTRY 4.0 APPLICATIONS, FDSE 2022 | 2022年 / 1688卷
关键词
Nonlinear diffusion equation; Neural network; Deep learning; Image denoising; EDGE-DETECTION; DIFFUSION; RESTORATION;
D O I
10.1007/978-981-19-8069-5_27
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:407 / 418
页数:12
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