基于维纳滤波并行小波变换模型的医学图像去噪算法

Lei Wang, Yun-Kang Zou, Hong-Jun Zhang
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引用次数: 6

摘要

针对医学CT图像中存在的噪声、边缘模糊、对比度差等问题,提出了一种基于小波多尺度变换维纳滤波的医学CT图像增强算法。采用小波多尺度分析将图像信号分解为不同方向的子图像。利用小波自适应维纳滤波器研究相应准则下不同方向的子图像信息,并采用并行形式对图像进行增强。通过对模型参数的估计,结合小波自适应维纳滤波器对其相应的尺度系数和小波系数进行去噪,得到小波重构后的增强图像。实验结果表明,所提出的并行范围模型算法能够在去除噪声的同时保留图像的关键特征,达到了较好的CT图像增强效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Medical Image Denoising Arithmetic Based on Wiener Filter Parallel Model of Wavelet Transform
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.
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