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Ultrasound Image Denoising Using Improved Image Decomposition Method

机译:超声图像去噪使用改进的图像分解方法

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Ultrasound images are very noisy. Along with system noise, a significant noise source is the speckle phenomenon caused by interference in the viewed object. The noise of ultrasound image was always deemed as multiplicative noise, but the present multiplicative noise models are not fitting for it very well, because the noise is more complex than the simple multiplicative one. So we should consider separate the real part from large noised image using image decomposition for the purpose of denoising. The VO (Vese-Osher) model can decompose image into smooth area and the oscillating patterns, but it unable to deal with image with large oscillating part very well. To deal with this problem, we modify the VO model by add a fourth order filter and we call this improved decomposition methods as mixted VO model. The combined algorithm takes the advantage of both filters since it is able to preserve edges and separate large oscillating part. The minimization problem must be realized through solving partial differential equations with complex finite difference scheme which leads to low efficiency. To simplify this, the Split Bregman algorithm for the mixted VO model is proposed and then we use it for ultrasound image denoising.
机译:超声图像非常嘈杂。随着系统噪声,显着的噪声源是由观察对象的干扰引起的散斑现象。超声图像的噪声始终被视为乘法噪声,但是本乘法噪声模型不适合它,因为噪声比简单的乘法更复杂。因此,我们应该考虑使用图像分解为去噪的目的的图像分解将实体部分分开。 VO(VESE-OSHER)模型可以将图像分解为平滑区域和振荡模式,但它无法处理大量振荡部分的图像。要处理此问题,我们通过添加第四阶滤波器修改VO模型,并将这种改进的分解方法称为混合的VO模型。组合算法需要两个过滤器的优点,因为它能够保护边缘和单独的大振荡部分。必须通过求解具有复杂有限差分方案的局部微分方程来实现最小化问题,这导致低效率。为了简化这一点,提出了混合VO模型的分割BREGMAN算法,然后我们将其用于超声图像去噪。

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