基于迭代正则化的自适应阈值hosvd图像去噪算法

Rodion Movchan, Zhengwei Shen
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引用次数: 2

摘要

本文提出了一种基于高阶奇异值分解(HOSVD)的三维图像去噪方法。我们采用了空间自适应迭代奇异值阈值(SAIST)的迭代正则化思想来设计算法,该算法的收敛速度比其他方法更快。采用并行计算技术实现该算法,大大降低了算法的计算复杂度。实验结果表明,在不同噪声水平下,该方法均能取得较好的PNSR效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive thresholding hosvd algorithm with iterative regularization for image denoising
In this paper, we propose a very simple 3D patch stack based image denoising method by Higher Order Singular Value Decomposition (HOSVD). We used the idea of iterative regularization from spatially adaptive iterative singular-value thresholding(SAIST) to design our algorithm, which indicates more faster convergence speed than some other methods. By using the parallel computing technique for implementing the algorithm, the computational complexity is highly reduced. The experiments also show good PNSR result with different noise levels.
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