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Image denoising by low-rank approximation with estimation of noise energy distribution in SVD domain

机译：估计SVD域中能量分布的低秩逼近图像降噪

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摘要

Low-rank approximation has shown great potential in various image tasks. It is found that there is a specific functional relationship about singular values between the original image and a series of noisy images, which can be used to construct the singular values of a noise-free image. In this study, the authors propose a novel denoising method based on the above facts and low-rank approximation theory. Firstly, they estimate the noise energy distribution of the group matrix in the singular value decomposition (SVD) domain using the energy characteristics of the image with different noise levels. The energy distribution of the noise is shrunk to obtain the energy distribution of the true signal. Then, based on the optimal energy compaction property of SVD, the low-rank property of matrix is constrained in the SVD domain to obtain the low-rank approximation of the matrix. Moreover, an iterative back projection method is adopted in this study to suppress residual noise. A new noise standard deviation estimation approach, targeted at the back projection process, is proposed to effectively optimise the denoising results during the iteration. Experimental results show that the authors' method efficiently decreases the noise and achieves comparable denoising performance to the state-of-the-art methods regarding both quantitative measurement and visual effect.
机译：低秩逼近已显示出在各种图像任务中的巨大潜力。发现原始图像和一系列噪声图像之间存在关于奇异值的特定功能关系，该关系可以用于构造无噪声图像的奇异值。在这项研究中，作者基于上述事实和低秩逼近理论提出了一种新颖的去噪方法。首先，他们利用具有不同噪声水平的图像的能量特征来估计奇异值分解（SVD）域中组矩阵的噪声能量分布。收缩噪声的能量分布以获得真实信号的能量分布。然后，基于SVD的最佳能量压缩特性，将矩阵的低秩特性约束在SVD域中以获得矩阵的低秩逼近。此外，本研究采用迭代反投影方法来抑制残留噪声。针对反投影过程，提出了一种新的噪声标准偏差估计方法，可以有效地优化迭代过程中的去噪结果。实验结果表明，与定量测量和视觉效果方面的最新技术相比，作者的方法可有效降低噪声并实现可比的降噪性能。

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  • 来源
    《Image Processing, IET》 |2019年第4期|680-691|共12页
  • 作者

    Fan Linwei; Meng Ran; Guo Qiang; Shi Miaowen; Zhang Caiming;

  • 作者单位

    Shandong Univ, Sch Comp Sci & Technol, ShunHua Rd 1500, Jinan, Shandong, Peoples R China|Shandong Coinnovat Ctr Future Intelligent Comp, BinHaiZhong Rd 191, Yantai, Peoples R China;

    Shandong Univ, Sch Comp Sci & Technol, ShunHua Rd 1500, Jinan, Shandong, Peoples R China;

    Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Bicycl East Rd 7366, Jinan, Shandong, Peoples R China;

    Shandong Univ, Sch Comp Sci & Technol, ShunHua Rd 1500, Jinan, Shandong, Peoples R China;

    Shandong Univ, Sch Comp Sci & Technol, ShunHua Rd 1500, Jinan, Shandong, Peoples R China|Shandong Coinnovat Ctr Future Intelligent Comp, BinHaiZhong Rd 191, Yantai, Peoples R China|Shandong Prov Key Lab Digital Media Technol, Bicycl East Rd 7366, Jinan 7366, Shandong, Peoples R China;

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