CN107067382A - 一种改进的图像边缘检测方法 - Google Patents
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Abstract
本发明公开了一种改进的图像边缘检测方法，包括以下步骤：S1：对图像进行平滑处理，使用改进的中值滤波器抑制噪声；S2：通过x，y，45°，135°方向上的一阶偏导数，求出水平和垂直方向的差分，进而求得梯度幅值和梯度方向；S3：对梯度幅值进行非极大值抑制；S4：利用梯度直方图求出高阈值和低阈值，然后利用双阈值算法对图像进行边缘检测；S5：锐化处理，并连接边缘，得到最终的边缘图像。本发明使用带权中值滤波代替高斯滤波，使用四个方向的偏导数求梯度幅值和方向，使用梯度直方图确定高低阈值，该方法降低了误检率，提高了检测精度，使得边缘图像轮廓更加清晰，连续性更良好。
Description
技术领域
本发明具体涉及一种改进的图像边缘检测方法。
背景技术
图像边缘是指其周围像素灰度不连续或极具变化的像素点的集合，也是目标、背景和区域之间的分界线。边缘检测首先检测图像中的边缘点，然后按照某种策略将边缘点连接成边缘线，最后构成分割区域。边缘检测是特征提取、目标识别、图像理解的基础，因此，它是图像处理与计算机视觉的基本问题。图像边缘检测主要通过求导算子、数学形态学、小波变换和图像融合等四种技术来实现，其中求导算子方式是最为广泛的边缘检测技术，包括Roberts算子、Sobel算子、Prewitt算子、Laplace算子和Log算子等，这些算子简单易于实现，实时性较好，但是对噪声的干扰敏感，抗干扰性差，边缘容易被噪声污染，检测效果不理想。
发明内容
本发明要解决的技术问题是提供一种改进的图像边缘检测方法。
一种改进的图像边缘检测方法，包括以下步骤：
S1：对图像进行平滑处理，使用改进的中值滤波器抑制噪声；
S2：通过x，y，45°，135°方向上的一阶偏导数，求出水平和垂直方向的差分，进而求得梯度幅值和梯度方向；
S3：对梯度幅值进行非极大值抑制；
S4：利用梯度直方图求出高阈值和低阈值，然后利用双阈值算法对图像进行边缘检测；
S5：锐化处理，并连接边缘，得到最终的边缘图像。
进一步的，步骤S1的具体方法如下：
1）为滤波区域内的每个位置分配一个独立的权值，权值的分配由权值矩阵来决定；在滤波计算过程在，每个像素值被***扩展的像素向量中，***次数为其权值，扩展向量为：，其中，；对此像素向量进行排序，然后以某个像素为中心，选取方形邻域，按邻域内的每个像素灰度值大小进行排序；
2）按照如下公式计算中间值y；
；
3）用中间输出值和周围像素灰度值作比较，将差值较大的像素值修改为与周围像素接近的值，以实现消除噪声点的目标。
进一步的，步骤S2的具体方法如下：
1）计算x，y，45°，135°方向上的一阶偏导数：
x方向：；
y方向：；
45°方向：；
135°方向：；
2）由以上四个方向的偏导数求得水平和垂直方向的差分为：
；
；
3）分别求得梯度幅值g和梯度方向：
；
。
本发明的有益效果是：
本发明使用带权中值滤波代替高斯滤波，使用四个方向的偏导数求梯度幅值和方向，使用梯度直方图确定高低阈值，该方法降低了误检率，提高了检测精度，使得边缘图像轮廓更加清晰，连续性更良好。
具体实施方式
以下具体实施例对本发明作进一步阐述，但不作为对本发明的限定。
一种改进的图像边缘检测方法，包括以下步骤：
S1：对图像进行平滑处理，使用改进的中值滤波器抑制噪声；
S2：通过x，y，45°，135°方向上的一阶偏导数，求出水平和垂直方向的差分，进而求得梯度幅值和梯度方向；
S3：对梯度幅值进行非极大值抑制；
S4：利用梯度直方图求出高阈值和低阈值，然后利用双阈值算法对图像进行边缘检测；
S5：锐化处理，并连接边缘，得到最终的边缘图像。
步骤S1的具体方法如下：
1）为滤波区域内的每个位置分配一个独立的权值，权值的分配由权值矩阵来决定；在滤波计算过程在，每个像素值被***扩展的像素向量中，***次数为其权值，扩展向量为：，其中，；对此像素向量进行排序，然后以某个像素为中心，选取方形邻域，按邻域内的每个像素灰度值大小进行排序；
2）按照如下公式计算中间值y；
；
3）用中间输出值和周围像素灰度值作比较，将差值较大的像素值修改为与周围像素接近的值，以实现消除噪声点的目标。
步骤S2的具体方法如下：
1）计算x，y，45°，135°方向上的一阶偏导数：
x方向：；
y方向：；
45°方向：；
135°方向：；
2）由以上四个方向的偏导数求得水平和垂直方向的差分为：
；
；
3）分别求得梯度幅值g和梯度方向：
；
。
Claims (3)
1.一种改进的图像边缘检测方法，其特征在于，包括以下步骤：
S1：对图像进行平滑处理，使用改进的中值滤波器抑制噪声；
S2：通过x，y，45°，135°方向上的一阶偏导数，求出水平和垂直方向的差分，进而求得梯度幅值和梯度方向；
S3：对梯度幅值进行非极大值抑制；
S4：利用梯度直方图求出高阈值和低阈值，然后利用双阈值算法对图像进行边缘检测；
S5：锐化处理，并连接边缘，得到最终的边缘图像。
2.根据权利要求1所述的图像边缘检测方法，其特征在于，步骤S1的具体方法如下：
1）为滤波区域内的每个位置分配一个独立的权值，权值的分配由权值矩阵来决定；在滤波计算过程在，每个像素值被***扩展的像素向量中，***次数为其权值，扩展向量为：，其中，；对此像素向量进行排序，然后以某个像素为中心，选取方形邻域，按邻域内的每个像素灰度值大小进行排序；
2）按照如下公式计算中间值y；
；
3）用中间输出值和周围像素灰度值作比较，将差值较大的像素值修改为与周围像素接近的值，以实现消除噪声点的目标。
3.根据权利要求1所述的图像边缘检测方法，其特征在于，步骤S2的具体方法如下：
1）计算x，y，45°，135°方向上的一阶偏导数：
x方向：；
y方向：；
45°方向：；
135°方向：；
2）由以上四个方向的偏导数求得水平和垂直方向的差分为：
；
；
3）分别求得梯度幅值g和梯度方向：
；
。
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