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Image denoising using multidirectional gradient domain
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DOI：10.1007/s11042-021-11184-5.png)
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摘要
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This paper presents a new two-step image denoising method termed multidirectional gradient domain image denoising (MGDID). In each step, unlike previous gradient domain designs, the multidirectional gradient domain information is used to represent the noise component so that the more directional image features are extracted. The Gaussian pre-filter is carried out in the square gradient coefficients. The nonlinear remedied factor is adopted to modify the denoising amount. The whole denoising process originates from classical nonlocal means (NLM) and nonlinear diffusion. MGDID takes full advantage of ability of NLM to better process the image with the rich repetitive features and the denoising scheme of relatively simplicity and efficiency of nonlinear diffusion. Experimental results show MGDID is superior to the related gradient domain methods and NLM methods in peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM) and visual performance. For example, for Barbara image with the rich repetitive texture feature, MGDID outperforms classical NLM from 0.33 dB to 1.66 dB in PSNR. Usually, classical NLM wins the local adaptive layered Wiener filer (a state-of-the-art gradient domain method) more than 0.44 dB for Barbara. In addition, MGDID is also very efficient compared to the related methods.
Keyword:
Image denoising
Nonlocal means (NLM)
Nonlinear diffusion
Gradient domain
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3
论文数:
2.0W
被引数:
3.2W
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