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A Medical Image Denoising Arithmetic Based on Wiener Filter Parallel Model of Wavelet Transform

机译:基于维纳滤波器并行模型的小波变换的医学图像去噪算法

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For the noise, blurry Edge, bad contrast of the medical CT image, a medical CT Image enhancing algorithm is presented based on Wiener filter of wavelet multi-scale transform in this article. Wavelet multi-scale analysis is used to decompose the image signal to different direction sub-image. It studies the different direction sub-pictorial information under the corresponding criterion by wavelet's auto-adapted Wiener filter, and enhances the image by parallel form. Through the estimation of the model parameter, combining wavelet's auto-adapted Wiener filter to denoise its corresponding coefficient of scale and its coefficient of wavelet, the enhanced image is obtained by wavelet reconstructing. The experimental result was shown that the proposed parallel scope model algorithm can retain the key image characters while removing the noise, and achieve a good enhancement of CT image.
机译:对于噪声,模糊边缘,医疗CT图像的不良对比度,基于本文中的小波多尺度变换的Wiener滤波器提出了一种医疗CT图像增强算法。小波多尺度分析用于将图像信号分解为不同的方向子图像。它研究了小波的自适华滤波器的相应标准下的不同方向子图示信息,并通过并行形式增强图像。通过估计模型参数,将小波的自适华滤波器组合以去噪其相应的规模系数及其小波系数,通过小波重建获得增强的图像。实验结果表明,所提出的并行范围模型算法可以在去除噪声的同时保留关键图像字符,并实现CT图像的良好增强。

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