基于模糊熵和结构特征的边缘检测方法

Edge Detection Method Based on Fuzzy Entropy and Structural Features

为了提高图像检测中的模糊边缘检测能力及抗噪性,提出了一种新的基于模糊熵与边缘结构特征的边缘检测方法.该方法首先利用非线性函数将图像的灰度值特征空间转换为模糊熵特征空间,以增强模糊边缘区域的对比;然后在像素的3×3邻域内定义12种有效的边缘结构,根据这些边缘结构提取图像中每个像素的结构信息测度图像阵列和方向信息测度图像阵列;最后对结构信息测度图像阵列和方向信息测度图像阵列实施非极大抑制,确定最终的边缘像素.实验结果表明,该方法具有较好的模糊边缘检测能力和抗噪性,所检测出的图像边缘细节丰富,单像素宽,定位准确.

In order to improve the detection ability of fuzzy image edge and robust to noise,this paper proposed a novel edge detection method based on the fuzzy entropy and the characters of image edge structure.In this method,a nonlinear function was used to transform the feature space of image gray levels into the one of fuzzy entropy so as to enhance the contrast of the fuzzy edge region.Then,twelve valid edge patterns were defined in a 3×3 neighborhood of the pixel and were used to extract the map arrays of the structure-and-direction-information measures for each pixel.Finally,the non-maximum suppression was performed for the two arrays to determine the final edge pi-xels. Experimental results show that the proposed algorithm performs well in terms of ability to detect fuzzy edge and robust to noise.The final edge image is precisely localized with single pixel.