An improved non-local mean filter filtering algorithm facing the cerebrovascular segmentation
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摘要: 介绍了经典非局部均值滤波算法与Manjn非局部均值滤波算法，改进了非局部均值滤波方法的相似度权值，使算法在具有旋转平移不变性，保持时间复杂度的同时优化了视觉效果与信噪比。实验通过添加噪声标准差从10~100不等的高斯加性噪声，比较了改进后的算法与传统滤波算法以及Manjn非均值滤波算法，结果表明，改进后的算法无论从视觉上还是数值上都优于Manjn非均值滤波算法。Abstract: We introduce the classical non-local means filtering algorithm and the improved non-local means filtering algorithm with the weight function modified by Manjn. In this paper, we propose different weight function, and make it have rotating shift invariance for the local windows while keeping the time complexity of optimizing the visual effect and SNR. By adding noise standard deviation from Gaussian additive noise ranging from 10 to 100, we compare the improved algorithms with traditional filtering algorithms and Manjn non-mean filtering algorithm. The results show that the improved algorithm from either visual or numerical is superior to Manjn non-mean filtering algorithm.
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Key words:
- medical image de-noising /
- cerebrovascular /
- non-local means filter(NLM)
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