Image Denoising Using the Higher Order Singular Value Decomposition
Image Denoising Using the Higher Order Singular Value Decomposition
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DOI：
10.1109/tpami.2012.140
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发表时间：
2013-04-01
影响因子：
23.6
通讯作者：
Banerjee, Arunava
中科院分区：
文献类型：
--
作者：
Rajwade, Ajit;Rangarajan, Anand;Banerjee, Arunava
在本文中，我们提出了一个非常简单和优雅的基于块的机器学习技术，使用高阶奇异值分解（HOSVD）进行图像去噪。该技术简单地将来自噪声图像的相似块（具有由统计激励标准定义的相似性）分组到3D堆栈中，计算该堆栈的HOSVD系数，通过硬阈值处理来操纵这些系数，并反转HOSVD变换以产生最终的滤波图像。我们的技术以原则性的方式选择所有所需的参数，将它们与噪声模型相关联。我们还讨论了我们的动机，采用HOSVD作为一个适当的变换图像去噪。我们的实验表明，该技术的灰度以及彩色图像的优良性能。在彩色图像上，我们的方法产生了最先进的结果，在中等高的噪声水平下优于其他彩色图像去噪算法。最佳补丁大小的选择和噪声方差估计的残差图像（去噪后）的标准。
In this paper, we propose a very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD). The technique simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final filtered image. Our technique chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image denoising. We experimentally demonstrate the excellent performance of the technique on grayscale as well as color images. On color images, our method produces state-of-the-art results, outperforming other color image denoising algorithms at moderately high noise levels. A criterion for optimal patch-size selection and noise variance estimation from the residual images (after denoising) is also presented.