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Speckle noise removal in ultrasound images using sparse code shrinkage

机译:使用稀疏代码收缩去除超声图像中的斑点噪声

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The paper proposes a method for ultrasound image denoising by using a classical signal processing method, i.e. Independent Component Analysis. The main idea is to process ultrasound images by the sparse code shrinkage algorithm based on ICA. We use the FastICA algorithm to estimate the inverse of the unknown mixing matrix and then apply the shrinkage operator for each determined independent component. The sparse code shrinkage method is compared with other speckle noise filtering algorithms and the results obtained show that sparse code shrinkage is a good method for multiplicative noise reduction in both test images and ultrasound images.
机译:本文提出了一种使用经典信号处理方法(即独立分量分析)的超声图像去噪方法。主要思想是通过基于ICA的稀疏代码收缩算法来处理超声图像。我们使用FastICA算法估计未知混合矩阵的逆,然后对每个确定的独立分量应用收缩算子。将稀疏代码收缩方法与其他散斑噪声过滤算法进行了比较,结果表明,稀疏代码收缩是一种在测试图像和超声图像中均能降低乘法噪声的好方法。

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