opencv中常用的线性滤波器--boxFilter(),blur(),GaussianBlur()

1. boxFilter()



下面是opencv官方对boxFilter()函数的介绍。如果均衡化（即normalize==ture，这也是默认值），则其本质是均值滤波。

C++: void boxFilter(InputArray src,
OutputArray dst, int ddepth, Size ksize, Point anchor=Point(-1,-1), boolnormalize=true, int borderType=BORDER_DEFAULT )
Python: cv2.boxFilter(src,
ddepth, ksize[, dst[, anchor[, normalize[,
borderType]]]]) → dst

Parameters:

src – input image. dst – output image of the same size and type as src. ddepth – the output image depth (-1 to use src.depth()). ksize – blurring kernel size. anchor – anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. normalize – flag, specifying whether the kernel is normalized by its area or not. borderType – border mode used to extrapolate pixels outside of the image.

The function smoothes an image using the kernel:



where



2. blur()

调用blur()等效于调用将normalize=true的boxFilter().

Blurs an image using the normalized box filter.
C++: void blur(InputArray src,
OutputArray dst, Size ksize, Point anchor=Point(-1,-1), intborderType=BORDER_DEFAULT )
Python: cv2.blur(src,
ksize[, dst[, anchor[, borderType]]]) →
dst

Parameters:

src – input image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F. dst – output image of the same size and type as src. ksize – blurring kernel size. anchor – anchor point; default value Point(-1,-1) means that the anchor is at the kernel center. borderType – border mode used to extrapolate pixels outside of the image.

The function smoothes an image using the kernel:



The call blur(src, dst, ksize, anchor, borderType) is
equivalent to boxFilter(src, dst, src.type(), anchor,true, borderType) .
3.GaussianBlur()

高斯滤波可以消除高斯噪声，广泛应用于图像处理的减噪过程。需要注意的是opencv中的GaussianBlur()是高斯低通滤波器，用来模糊减噪，所以叫高斯模糊。

整数模板用的比较多，常见的3x3或者5x5的整数模板如下。更多高斯滤波的讲解可以参考下面这篇博客http://blog.csdn.net/yansmile1/article/details/46275791



4. 应用实例

分别调用上面提到的三个函数对一副图像进行模糊操作，选取的kernel size为5x5。代码如下

#include<opencv.hpp>
#include<iostream>

int main(void)
{
cv::Mat src = cv::imread("d:/Opencv Picture/Lena.jpg", CV_LOAD_IMAGE_GRAYSCALE);

if (!src.data)
{
std::cout << "image read error!!!\n";
}
cv::imshow("src",src);

//call boxFilter()
cv::Mat boxFilterDst;
cv::boxFilter(src, boxFilterDst, -1, cv::Size(5, 5));
cv::imshow("boxFilterResult", boxFilterDst);

//call blur()
cv::Mat blurDst;
cv::blur(src, blurDst, cv::Size(5, 5));
cv::imshow("blurResult", blurDst);

//call GaussianBlur()
cv::Mat GaussianDst;
cv::GaussianBlur(src, blurDst, cv::Size(5, 5),0.8,0.8);
cv::imshow("GaussianBlurResult", blurDst);

cvWaitKey(0);

return 0;
}

运行结果如下，明显在相同的kernel size的情况下，GaussianBlur()的结果对原图的失真比较少（当然还和sigmaX & sigmaY有关），而boxFilter & blur()得到的结果是相同的，因为都是均值滤波。