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Ultrasound Image Denoising Using Improved Image Decomposition Method

机译:改进的图像分解方法对超声图像进行去噪

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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。与系统噪声一起,重要的噪声源是由对被查看对象的干扰引起的斑点现象。超声图像的噪声始终被认为是乘法噪声,但是当前的乘法噪声模型并不十分适合它,因为该噪声比简单的乘法噪声更为复杂。因此,出于降噪的目的,我们应该考虑使用图像分解将实部与大噪点图像分开。 VO(Vese-Osher)模型可以将图像分解为平滑区域和振荡模式,但无法很好地处理较大振荡部分的图像。为了解决这个问题,我们通过添加四阶滤波器来修改VO模型,并将这种改进的分解方法称为混合VO模型。组合算法利用了两个滤波器的优势,因为它能够保留边缘并分离较大的振荡部分。最小化问题必须通过用复杂的有限差分方案求解偏微分方程来实现,从而导致效率低下。为了简化此过程,提出了混合VO模型的Split Bregman算法,然后将其用于超声图像去噪。

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