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Titlebook: Despeckling Methods for Medical Ultrasound Images; Ju Zhang,Yun Cheng Book 2020 Springer Nature Singapore Pte Ltd. 2020 Despeckling Method

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Despeckle Filtering of Medical Ultrasonic Images Using Wavelet and Guided Filter,lter. The filtered image is then obtained by using the inverse wavelet transformation. Experiments with the comparison of the other seven de-speckling filters are conducted. The results show that the proposed method not only has a strong de-speckling ability, but also keeps the image details, such as the edge of a lesion.
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Wavelet and Fast Bilateral Filter Based Despeckling Method for Medical Ultrasound Images,al properties of medical ultrasound image in the wavelet domain, an improved wavelet threshold function based on the universal wavelet threshold function is considered. The wavelet coefficients of noise-free signal and speckle noise are modeled as generalized Laplace distribution and Gaussian distri
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Despeckle Filtering of Medical Ultrasonic Images Using Wavelet and Guided Filter,ion is applied to obtain a wavelet shrinkage algorithm. The coefficients of the low frequency sub-band in the wavelet domain are filtered by guided filter. The filtered image is then obtained by using the inverse wavelet transformation. Experiments with the comparison of the other seven de-speckling
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Despeckling Method for Medical Images Based on Wavelet and Trilateral Filter,mic additive model is developed to account for the medical ultrasound signal with speckle noise. Secondly, in accordance with the statistical property of the additive model, an adaptive wavelet shrinkage algorithm is applied to the noisy medical signal. Particularly, the algorithm is significant to
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Nonsubsampled Shearlet and Guided Filter Based Despeckling Method for Medical Ultrasound Images,placian pyramid filter is used to decompose the noisy image thus decomposing the image into high-frequency and low-frequency subbands. Under the direction of the non-sampling filter bank, a highfrequency subband multi-directional decomposition is obtained. Next, based on the threshold function and t
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