最优非高斯噪声抑制的自适应序统计滤波器

2011 Western New York Image Processing Workshop Pub Date : 2011-11-01 DOI:10.1109/WNYIPW.2011.6122886

M. Fernández

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引用次数: 0

摘要

本文提出了一种使图像信噪比增益最大化的自适应序统计滤波器(OSF)。特别是，这种与分布无关的非线性滤波器在噪声不是高斯的情况下近似于最优滤波器(例如，散斑型杂波，伽马噪声等)。仿真结果定量地证明了自适应OSF在非高斯噪声存在下优于常用的线性和非线性替代方法的性能。

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Adaptive Order-Statistic Filters for optimal non-Gaussian noise suppression

This paper presents an adaptive Order-Statistic Filter (OSF) that maximizes the gain in image SNR. In particular, this distribution-independent non-linear filter approximates the optimal filter when the noise is not Gaussian (e.g., speckle-type clutter, Gamma noise, etc.). Simulation results quantitatively demonstrate the superior performance of the adaptive OSF over popular linear and non-linear alternatives in the presence of non-Gaussian noise.

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2011 Western New York Image Processing Workshop

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