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Despeckling Algorithm on Ultrasonic Image Using Adaptive Block-Based Singular Value Decomposition

机译:基于自适应块奇异值分解的超声图像去斑算法

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Speckle noise reduction is an important technique to enhance the quality of ultrasonic image. In this paper, a despeckling algorithm based on an adaptive block-based singular value decomposition filtering (BSVD) applied on ultrasonic images is presented. Instead of applying BSVD directly to ultrasonic image, we propose to apply BSVD on the noisy edge image version obtained from the difference between the logarithmic transformations of the original image and blur image version of its. The recovered image is performed by combining the speckle noise-free edge image with blur image version of its. Finally, exponential transformation is applied in order to get the reconstructed image. To evaluate our algorithm compared with well-know algorithms such as Lee filter, Kuan filter, Homomorphic Wiener filter, median filter and wavelet soft thresholding, four image quality measurements, which are Mean Square Error (MSE), Signal to MSE (S/MSE), Edge preservation (β), and Correlation measurement (ρ), are used. From the results, it clearly shows that the proposed algorithm outperforms other methods in terms of quantitative and subjective assessments.
机译:减少斑点噪声是提高超声图像质量的重要技术。提出了一种基于自适应块奇异值分解滤波(BSVD)的超声图像去斑算法。代替直接将BSVD应用于超声图像,我们建议将BSVD应用于从原始图像的对数转换与其模糊图像版本之间的差异获得的嘈杂边缘图像版本。通过将无斑点噪声边缘图像与其模糊图像版本相结合来执行恢复的图像。最后,应用指数变换以获得重构图像。为了对我们的算法进行评估,并与诸如Lee滤波器,Kuan滤波器,同态Wiener滤波器,中值滤波器和小波软阈值处理,四种图像质量测量(均方误差(MSE),MSE信号(S / MSE))的知名算法进行比较),边缘保留(β)和相关性测量(ρ)。从结果可以清楚地看出,在定量和主观评估方面,该算法优于其他方法。

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