边缘是图象的基本特征，边缘信息是进行图象分析和识别的重要属性，但由于常用的边缘提取方法在提取边缘的同时，容易丢失图象的细节边缘信息，为此提出了一种基于灰度形态学和图象分解技术相结合的图象细节边缘提取方法，该方法首先运用灰度形态学方法检测出包含图象细节的边缘图象并去除部分背景和噪声，然后进行区域分解，再通过对不同的区域选取不同的阈值来保证边缘提取的完整性．仿真结果表明，与传统方法相比，该方法能有效地提取一般图象的细节边缘，甚至能提取被噪声污染图象的边缘．

It is well known that edge is the basic feature of image and the important property for image analysis and recognition. Using traditional edge extraction, strong edge can be efficiently extracted but detail edge information may be lost, however, these detail edge information are often important features in some real applications. This paper proposes a new method to extract image detail edge based on the combination of gray-morphology and image decomposition. First gray-morphological operators are used to detect edge image and remove part background and noise, then it is decomposed into several areas using quad-tree method, continuous decomposition is terminated when the area size is equal or smaller than the minimal area size parameter, finally, different thresholds for different areas are selected to ensure the integrality of edge extraction, in order to void smooth background are involved and some detail edges are lost, global minimal and maximal thresholds are set beforehand to limit the scope of selected threshold, when area threshold is smaller than global minimal threshold, it will be replaced by the global minimal threshold, inversely, by global maximal threshold. Simulations show that this method can efficiently extract detail edges from both noiseless images and noise images.