提出一种改进的自适应广义形态滤波器。
An improved adaptive generalized morphological filter is proposed.
通过仿真比较，验证了在噪声抑制和细节保持方面广义形态滤波器比传统形态滤波器有较好的性能。
By the simulations and comparisons, we verify that the GMFs have better performances than the traditional morphological filters in noise-suppressing and detail-preserving.
在对数学形态学算子和传统形态滤波器研究的基础上，提出了基于自适应LMS算法的广义形态滤波器。
On the basis of the research of the morphological operator and traditionally morphology filter, the paper proposes the generalized morphology filter founded on LMS adaptive algorithm.
在建立广义形态滤波器与层迭滤波器关系的基础上，分析了各种输入分布时，广义形态滤波器输出的统计特性。
We set up the relations between the GMFs and the stack filters, and analyzed the output statistical properties of the GMFs under various input distributions.
在建立广义形态滤波器与层迭滤波器关系的基础上，分析了各种输入分布时，广义形态滤波器输出的统计特性。
We set up the relations between the GMFs and the stack filters, and analyzed the output statistical properties of the GMFs under various input distributions.
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