Image Global K-SVD Variational Denoising Method Based on Wavelet Transform


Chang Wang, Wen Zhang, Journal of Information Processing Systems Vol. 19, No. 3, pp. 275-288, Jun. 2023  

https://doi.org/10.3745/JIPS.04.0277
Keywords: High frequency, Image Global K-singular Value Decomposition (IGK-SVD) Method, Low frequency, Variational Denoising Method (VDM), Wavelet Decomposition
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Abstract

Many image edge details are easily lost in the image denoising process, and the smooth image regions are prone to produce jagged. In this paper, we propose a wavelet-based image global k- singular value decomposition variational method to remove image noise. A layer of wavelet decomposition is applied to the noisy image first. Then, the image global k-singular value decomposition (IGK-SVD) method is used to remove the random noise of low-frequency components. Furthermore, a constructed variational denoising method (VDM) removes the random noise in the high-frequency component. Finally, the denoised image is obtained by wavelet reconstruction. The experimental results show that the proposed method's peak signal-to-noise ratio (PSNR) value is higher than other methods, and its structural similarity (SSIM) value is closer to one, indicating that the proposed method can effectively suppress image noise while retaining more image edge details. The denoised image has better denoising effects.


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Cite this article
[APA Style]
Wang, C. & Zhang, W. (2023). Image Global K-SVD Variational Denoising Method Based on Wavelet Transform. Journal of Information Processing Systems, 19(3), 275-288. DOI: 10.3745/JIPS.04.0277.

[IEEE Style]
C. Wang and W. Zhang, "Image Global K-SVD Variational Denoising Method Based on Wavelet Transform," Journal of Information Processing Systems, vol. 19, no. 3, pp. 275-288, 2023. DOI: 10.3745/JIPS.04.0277.

[ACM Style]
Chang Wang and Wen Zhang. 2023. Image Global K-SVD Variational Denoising Method Based on Wavelet Transform. Journal of Information Processing Systems, 19, 3, (2023), 275-288. DOI: 10.3745/JIPS.04.0277.