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Stack filters and selection probabilities

机译:堆栈过滤器和选择概率

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Based on the fact that the output of a given stack filter can be determined if the ranks of the samples in the input window are known and that this output always equals one of the samples in the input window, rank and sample selection probabilities are defined. The output distribution of the stack filter of size N with independent identically distributed (i.i.d.) inputs can be expressed as a weighted sum of the ith, i=1, 2, ..., N order statistics, where the rank selection probabilities are the weights. The sample selection probabilities equal the impulse response coefficients of a finite impulse response (FIR) filter whose output spectrum is closest, of all linear filters, to that of the stack filter for i.i.d. Gaussian inputs. Results are also derived for correlated inputs. Robustness and detail preserving properties of stack filters are related to the selection probabilities. Other statistical properties are also derived. Finally, methods to compute the selection probabilities of the stack filter from its positive Boolean function and the selection probabilities of the weighted median filter from its weights are given in detail.
机译:基于以下事实:如果已知输入窗口中样本的秩,并且该输出始终等于输入窗口中的样本之一,则可以确定给定堆栈过滤器的输出,从而定义了秩和样本选择概率。大小为N且具有独立的相同分布(iid)输入的堆栈滤波器的输出分布可以表示为ith的加权和,即i = 1、2,...,N阶统计量,其中秩选择概率为重量。在所有线性滤波器中,样本选择概率等于有限脉冲响应(FIR)滤波器的脉冲响应系数,该滤波器的输出频谱在所有线性滤波器中都最接近于i.i.高斯输入。还可以得出相关输入的结果。堆栈过滤器的鲁棒性和细节保留特性与选择概率有关。还导出其他统计属性。最后,详细介绍了从堆栈过滤器的正布尔函数中计算选择概率和从加权中值滤波器的加权中选择概率的方法。

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