Image recognition in the presence of non-Gaussian noise with unknown statistics.
Image recognition in the presence of non-Gaussian noise with unknown statistics.
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DOI：
10.1364/josaa.18.002744
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发表时间：
2001-11
期刊：
影响因子：
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通讯作者：
N. Towghi;B. Javidi
中科院分区：
文献类型：
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作者：
N. Towghi;B. Javidi
我们设计的接收器，以检测一个已知的模式或参考信号的存在非常普遍和非高斯类型的噪声。三个来源的输入噪声退化被认为是：添加剂，乘性，和不相交的背景。检测过程包括两个步骤：（1）在假设检验的框架内估计相关噪声参数;（2）最大化测量目标位于给定位置的可能性的某个度量。参数估计部分通过矩匹配技术来进行。由于未知参数的数量以及各种类型的输入噪声过程是非高斯的事实，用于估计这些参数的方法不同于最大化似然函数的标准方法。为了验证目标在某个位置的存在，我们使用l（p）-范数度量（p >或= 0）来测量目标存在于感兴趣位置的可能性。计算机模拟表明，对于这里测试的图像，这里设计的接收机比一些现有的接收机性能更好。
We design receivers to detect a known pattern or a reference signal in the presence of very general and non-Gaussian types of noise. Three sources of input-noise degradation are considered: additive, multiplicative, and disjoint background. The detection process involves two steps: (1) estimation of the relevant noise parameters within the framework of hypothesis testing and (2) maximizing a certain metric that measures the likelihood of the target being at a given location. The parameter estimation portion is carried out by moment-matching techniques. Because of the number of unknown parameters and the fact that various types of input-noise processes are non-Gaussian, the methods that are used to estimate these parameters differ from the standard methods of maximizing the likelihood function. To verify the existence of the target at a certain location, we use l(p)-norm metric for p > or = 0 to measure the likelihood of the target being present at the location of interest. Computer simulations are used to show that for the images tested here, the receivers designed herein perform better than some existing receivers.