强噪声下自适应Canny算子边缘检测

针对传统Canny算子不能有效滤除图像在解码处理和传输过程产生的椒盐噪声、无法保留边缘细节的问题,提出强噪声下Canny算子图像边缘检测算法。依据椒盐噪声的极值性、灰度差值性,将像元点划分为噪声点、疑似噪声点;根据分类之后的像元点自适应地改变滤波器窗口的大小和权值,在降低噪声影响的同时能较好地保留图像细节。引入8个方向模板的Sobel算子计算梯度幅值以提高滤波后的边缘定位效果。使用迭代自适应阈值算法与Otsu算法选择最佳阈值,实现阈值自适应设定,提高边缘连接效果。实验结果表明:图像去噪后的结构相似度为0.949,峰值信噪比相较于传统算法提升了10.97 dB。边缘评价指标提高27.2%,F1值提高了34.6%。该算法能有效去除椒盐噪声,具有更好的边缘细节保护能力。

Adaptive Canny operator edge detection under strong noise

The traditional Canny operator cannot effectively filter out the salt and pepper noise generated during the decoding process and transmission of an image,and cannot retain the edge details.To overcome this,an improved Canny operator image edge detection algorithm for operation under strong noise was proposed.According to the extreme value and gray difference of salt and pepper noise,the pixel points were divided into noise points and suspected noise points.The size and weight of the filter window were adaptively changed according to the pixel points after classification,which could reduce the influence of noise while retaining the image details.Then,the Sobel operators for eight directional templates were introduced to calculate the gradient amplitude to improve the edge positioning effect after filtering.Finally,iterative adaptive threshold algorithm and Otsu algorithm were used to select the best threshold to achieve adaptive threshold setting and improve the edge connection effect.The results of the comparative experiment show that after denoising the noisy image,the structural similarity is 0.949,the peak signal-tonoise ratio is increased by 10.97 dB compared with the traditional algorithm,the average edge evaluation is increased by 27.2%,and the F1 value is increased by 34.6%.The proposed algorithm retains the excellent performance of the Canny operator,can effectively remove salt and pepper noise,and has better edge detail protection capabilities.