Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Image Global K-SVD Variational Denoising Method Based on Wavelet Transform

  • Chang Wang (School of Civil Engineering, University of Science and Technology Liaoning) ;
  • Wen Zhang (School of Civil Engineering, University of Science and Technology Liaoning)
  • Received : 2022.03.29
  • Accepted : 2023.01.01
  • Published : 2023.06.30
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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.

Keywords

Acknowledgement

This research was supported by the Fund project of the Provincial Education Department (No. LJKMZ20220638) and the Open Fund Project of the Marine Information Technology Innovation Center of the Ministry of Natural Resources. We appreciate the time and thought given by the anonymous reviewers who diligently reviewed this letter and gave valuable feedback.

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