Image Global K-SVD Variational Denoising Method Based on Wavelet Transform
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초록
Many image edge details are easily lost in the image denoising process, and the smooth image regions are proneto produce jagged. In this paper, we propose a wavelet-based image global k- singular value decompositionvariational 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 noiseof low-frequency components. Furthermore, a constructed variational denoising method (VDM) removes therandom 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 ishigher than other methods, and its structural similarity (SSIM) value is closer to one, indicating that theproposed method can effectively suppress image noise while retaining more image edge details. The denoisedimage has better denoising effects.
Abstract
Many image edge details are easily lost in the image denoising process, and the smooth image regions are proneto produce jagged. In this paper, we propose a wavelet-based image global k- singular value decompositionvariational 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 noiseof low-frequency components. Furthermore, a constructed variational denoising method (VDM) removes therandom 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 ishigher than other methods, and its structural similarity (SSIM) value is closer to one, indicating that theproposed method can effectively suppress image noise while retaining more image edge details. The denoisedimage has better denoising effects.
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