【摘要】 针对传统的基于小波变换的边缘检测法无法提取低频区域完整连续的边缘,并且会丢弃包含一些重要细节的高频区域,同时受到噪声的影响而导致边缘提取效果不佳的问题,提出了一种基于融合技术的小波变换和数学形态学的边缘检测算法.在小波域中,对低频子图像采用数学形态学进行边缘检测,对高频子图像先进行小波降噪再采用小波模极大值法进行加权边缘检测,最后采用一定的融合规则对高、低频边缘子图像进行融合.实验结果表明,该方法用于图像边缘提取,不但能有效去除噪声干扰,又能突出边缘细节,边缘定位连续准确.更多还原

【Abstract】 Edge detection based on wavelet transform that can’t extract the full and continuous edge of the low-frequency sub-image and just makes use of the high-frequency sub-images may discard some important details of the image and detect poor edges because of image noises effect.Aiming at these problems,this paper proposes a new fusion algorithm based on wavelet transform and mathematical morphology to detect image edges.In the wavelet domain,the low-frequency sub-image edges are detected by mathematical morphology,while the high-frequency sub-images are detected by solving the maximum points of local wavelet coefficient model to restore edges after reducing the noise.Then,the final edge images are obtained through wavelet composition from the fusion edge sub-images according to certain fusion rules.Experimental results show the proposed method can detect image edges not only remove the noise effectively but also enhance the edges and locate edges accurately.更多还原