WGAN-Based Image Denoising Algorithm

XiuFang Zou, Dingju Zhu, Jun Huang, Wei Lu, Xinchu Yao, Zhaotong Lian

Source Title: Journal of Global Information Management (JGIM)30(9)

ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668446577|DOI: 10.4018/JGIM.300821

MLA

Zou, XiuFang, et al. "WGAN-Based Image Denoising Algorithm." JGIM vol.30, no.9 2022: pp.1-20. http://doi.org/10.4018/JGIM.300821

APA

Zou, X., Zhu, D., Huang, J., Lu, W., Yao, X., & Lian, Z. (2022). WGAN-Based Image Denoising Algorithm. Journal of Global Information Management (JGIM), 30(9), 1-20. http://doi.org/10.4018/JGIM.300821

Chicago

Zou, XiuFang, et al. "WGAN-Based Image Denoising Algorithm," Journal of Global Information Management (JGIM) 30, no.9: 1-20. http://doi.org/10.4018/JGIM.300821

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Abstract

Traditional image denoising algorithms are generally based on spatial domains or transform domains to denoise and smooth the image. The denoised images are not exhaustive, and the depth-of-learning algorithm has better denoising effect and performs well while retaining the original image texture details such as edge characters. In order to enhance denoising capability of images by the restoration of texture details and noise reduction, this article proposes a network model based on the Wasserstein GAN. In the generator, small convolution size is used to extract image features with noise. The extracted image features are denoised, fused and reconstructed into denoised images. A new residual network is proposed to improve the noise removal effect. In the confrontation training, different loss functions are proposed in this paper.