Affiliations: [a] School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
| [b] School of Information and Electricity Engineering, Hebei University of Engineering, Handan, China
| [c] School of Information Engineering, Handan College, Handan, China
Abstract: Edge detection is image processing, analysis and one of the most important areas of research in the field of computer vision; it is the basic tools of pattern recognition and image information extraction. Actual image processing is generally mixed with noise. How to eliminate the false edge caused by noise interference and ensure the accuracy of edge positioning, it becomes an important problem to be solved in edge detection and is also the purpose of this paper. Firstly, a histogram matching image enhancement algorithm based on maximum fuzzy entropy dynamic improvement is proposed. The algorithm first maps gray scale images from the spatial domain to the fuzzy domain, the target image is divided into several gray layers based on maximum fuzzy entropy. And then, for the characteristics of different gray levels, histogram matching method is used to design corresponding matching function for each gray layer. These matching functions are used to enhance the corresponding gray layer to obtain the enhanced image. Image enhancement method combines fuzzy entropy and histogram matching algorithm, it can effectively suppress noise and improve image contrast ratio. Secondly, an image edge detection algorithm based on improved fuzzy theory is proposed. This algorithm uses the improved fuzzy enhancement algorithm to enhance the original image. The non-maximum suppression algorithm is used to process the enhanced image; the optimal threshold value is obtained by fuzzy extraction and maximum inter-group variance method. This algorithm is used for edge detection of image. Experiments show that the algorithm is feasible and effective, and has some advantages.
