A Novel Conditional Generative Adversarial Network Based On Graph Attention Network For Moving Image Denoising

Weihong Shen

doi:10.6180/jase.202306_26(6).0009

A Novel Conditional Generative Adversarial Network Based On Graph Attention Network For Moving Image Denoising

Computer Science and Information Engineering

Weihong Shen This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹Art and Sports Department, Henan Technical College of Construction, Zhengzhou 450064 China

Received: July 17, 2022
Accepted: August 8, 2022
Publication Date: September 15, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202306_26(6).0009

ABSTRACT

Aiming at the problem that the noise in the process of image acquisition and transmission leads to the degradation of the subsequent image processing capability, we propose a conditional generative adversarial network (CGAN) based on graph attention network for moving image denoising. The CGAN can effectively extract depth features and avoid losing details. The discriminant network is constructed based on fully convolutional network, so pixel classification can be obtained to improve the accuracy of discrimination. In addition, in order to improve the denoising ability and preserve the image details as much as possible, a graph attention network is proposed. The compound loss function is constructed based on confrontation loss, visual perception loss and mean square error loss. Finally, the adaptive weighted average is used to fuse the three-channel output information to obtain the final denoised image. Experimental results show that compared with other state-of-the-art denoising algorithms, the proposed algorithm can effectively remove image noise and restore original image details.

Keywords: CGAN; Graph attention network; Moving image denoising; Adaptive weighted average

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