Background and objective

In the observed medical ultrasound image, there is always some speckle noise which suppress the details of images and impairs the value of ultrasonography in diagnosis. This work present a novel despeckling method which effectively exploit non-local self-similarity for restoration of corrupted ultrasound images. The proposed approach consist of three stages. First, an improved optimized Bayesian non-local means (OBNLM) filter in which pixel patch is represented by a new vector form is used to get an preliminary estimation of noise-free image. Then, a new index called redundancy index of each pixel patch is calculated for determining which areas in image have low redundancy. Finally, another new vector form is used to represent pixel patch in areas with low redundancy obtained in second stage to recalculate filtered output, and the recalculated output is superimposed on preliminary estimation to generate final result of proposed method.

Methods

The performance of proposed approach is evaluated on simulated and real ultrasound images. The experiments conducted on various test image illustrate that our proposed algorithm outperforms the various classic denoising algorithms included block matching 3-D (BM3D) and optimized Bayesian non-local means filter.

Results

The objective evaluations and subjective visual inspection of denoised simulated and real ultrasound images demonstrate that the proposed algorithm can achieve superior performance than previously developed methods for speckle noise suppression.

Conclusions

The combined use of two new representations improve denoising and edge preserving capability of proposed filter apparently. The success of proposed algorithm would help in building the lay foundation for inventing the despeckling algorithms that can make fuller use of information in images.

中文翻译：

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改进的非局部自相似性度量可有效降低超声图像中的斑点噪声。

背景和目标

在所观察的医学超声图像中，总是会出现一些斑点噪声，这些噪声会抑制图像的细节并损害超声检查在诊断中的价值。这项工作提出了一种新颖的去斑点方法，该方法可以有效地利用非局部自相似性来恢复损坏的超声图像。提议的方法包括三个阶段。首先，使用改进的优化贝叶斯非局部均值（OBNLM）滤波器（其中像素斑块由新矢量形式表示）来获得无噪声图像的初步估计。然后，计算一个新的索引，称为每个像素补丁的冗余索引，以确定图像中哪些区域的冗余度较低。最后，另一种新的矢量形式用于表示第二阶段获得的低冗余区域中的像素补丁，以重新计算滤波后的输出，

方法

在模拟和真实超声图像上评估所提出方法的性能。在各种测试图像上进行的实验表明，我们提出的算法优于包括块匹配3-D（BM3D）和优化的贝叶斯非局部均值滤波器在内的各种经典降噪算法。

结果

对降噪后的模拟和真实超声图像进行客观评估和主观视觉检查，结果表明，与以前开发的散斑噪声抑制方法相比，该算法可以实现更高的性能。

结论

两种新表示的组合使用明显改善了所提出滤波器的去噪和边缘保留能力。所提算法的成功将有助于为发明去斑点算法奠定基础，从而可以充分利用图像中的信息。