基于信息理论的Sar图像散斑降噪

D. Chan, J. Gambini, A. Frery
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引用次数: 1

摘要

本文提出了一种基于Shannon熵和Renyi熵的非局部均值滤波方法。测量中心窗口和图像斑块之间的相似性是基于比较两个样本是否具有相同的熵从而具有相同的分布的统计测试。结果令人鼓舞,滤波后的图像具有较好的信噪比,保持了均值,边缘没有严重模糊。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speckle Noise Reduction In Sar Images Using Information Theory
In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution.The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred.
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