为了能有效滤除散斑噪声, 提出了一种以模糊系统为基础的散斑噪声滤波器。这种滤波器包括了一个模糊推理系统, 一个边缘检测和膨胀模块和一个图像合成器。模糊推理系统包括 5个输入和 1个输出, 负责对散斑噪声图像进行滤波处理, 其参数通过克隆选择优化算法训练优化得到。边缘检测和膨胀模块用于区分图像的边缘区域和光滑区域, 图像合成器则是根据边缘检测和膨胀模块获取的信息分区域对滤波输出图像进行合成。文中将该方法与其他几种常用的散斑噪声滤除方法进行了比较, 实验结果表明, 与其他方法相比该方法可以显着减少图像的散斑噪声, 同时保留边缘、细节等有价值的信息。

In this paper, a novel fuzzy system-based filter for speckle noise removal is proposed. The proposed filter consists of a fuzzy inference system, an edge detection and dilation unit, and an image combiner. The fuzzy inference system includes 5 inputs and 1 output, and it is responsible for filtering the speckle noisy image. The edge detection and dilation unit is used for classifying the uniform areas and edge areas. The image combiner unites the output images according to the information coming from the edge detection and dilation unit. The training phase of the fuzzy inference system is implemented by using the clonal selection optimization algorithm with appropriate training data. The performance of the proposed method is compared with popular speckle noise removal filters available in the literature by performing extensive simulations. The experimental results show that the proposed method can significantly reduce the speckle noise from digital images while preserving edges, and valuable details.