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

约定不等于承诺〃 2022-06-13 09:24 496阅读 0赞

**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), bool **normalize**=true, int **borderType**=BORDER\_DEFAULT )

**Python:**cv2. boxFilter (src, ddepth, ksize \[, dst \[, anchor \[, normalize \[, borderType \] \] \] \] ) → dst

<table style="border-collapse:collapse; margin:20px -.5em 10px; border:0px"> 
 <tbody> 
 <tr> 
 <th style="text-align:left; padding:1px 8px 1px 5px; border:0px!important"> Parameters:</th> 
 <td style="padding:1px 8px 1px 5px; border:0px!important"> 
 <ul style="margin:0px; padding-left:1em"> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">src&nbsp;– input image.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">dst&nbsp;– output image of the same size and type as&nbsp;src.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">ddepth&nbsp;– the output image depth (-1 to use&nbsp;src.depth()).</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">ksize&nbsp;– blurring kernel size.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">anchor&nbsp;– anchor point; default value&nbsp;Point(-1,-1)&nbsp;means that the anchor is at the kernel center.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">normalize&nbsp;– flag, specifying whether the kernel is normalized by its area or not.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">borderType&nbsp;– border mode used to extrapolate pixels outside of the image.</li> 
 </ul> </td> 
 </tr> 
 </tbody> 
</table>

The function smoothes an image using the kernel:

![\\texttt\{K\} =  \\alpha \\begin\{bmatrix\} 1 & 1 & 1 &  \\cdots & 1 & 1  \\\\ 1 & 1 & 1 &  \\cdots & 1 & 1  \\\\ \\hdotsfor\{6\} \\\\ 1 & 1 & 1 &  \\cdots & 1 & 1 \\end\{bmatrix\}][texttt_K_ _ _alpha _begin_bmatrix_ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ _hdotsfor_6_ _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _end_bmatrix]

where

![\\alpha = \\fork\{\\frac\{1\}\{\\texttt\{ksize.width\*ksize.height\}\}\}\{when \\texttt\{normalize=true\}\}\{1\}\{otherwise\}][alpha _ _fork_frac_1_texttt_ksize.width_ksize.height_when _texttt_normalize_true_1_otherwise]

**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), int **borderType**=BORDER\_DEFAULT )

**Python:**cv2. blur (src, ksize \[, dst \[, anchor \[, borderType \] \] \] ) → dst

<table style="border-collapse:collapse; margin:20px -.5em 10px; border:0px"> 
 <tbody> 
 <tr> 
 <th style="text-align:left; padding:1px 8px 1px 5px; border:0px!important"> Parameters:</th> 
 <td style="padding:1px 8px 1px 5px; border:0px!important"> 
 <ul style="margin:0px; padding-left:1em"> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">src&nbsp;– input image; it can have any number of channels, which are processed independently, but the depth should be&nbsp;CV_8U,&nbsp;CV_16U,&nbsp;CV_16S,&nbsp;CV_32F&nbsp;or&nbsp;CV_64F.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">dst&nbsp;– output image of the same size and type as&nbsp;src.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">ksize&nbsp;–&nbsp;blurring kernel size.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">anchor&nbsp;– anchor point; default value&nbsp;Point(-1,-1)&nbsp;means that the anchor is at the kernel center.</li> 
 <li style="text-align:justify; line-height:18.2px; margin:0px">borderType&nbsp;– border mode used to extrapolate pixels outside of the image.</li> 
 </ul> </td> 
 </tr> 
 </tbody> 
</table>

The function smoothes an image using the kernel:

![\\texttt\{K\} =  \\frac\{1\}\{\\texttt\{ksize.width\*ksize.height\}\} \\begin\{bmatrix\} 1 & 1 & 1 &  \\cdots & 1 & 1  \\\\ 1 & 1 & 1 &  \\cdots & 1 & 1  \\\\ \\hdotsfor\{6\} \\\\ 1 & 1 & 1 &  \\cdots & 1 & 1  \\\\ \\end\{bmatrix\}][texttt_K_ _ _frac_1_texttt_ksize.width_ksize.height_ _begin_bmatrix_ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ _hdotsfor_6_ _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ _end_bmatrix]

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][http_blog.csdn.net_yansmile1_article_details_46275791]

![1365044411_7461.JPG][]

**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。

![Center][]

[texttt_K_ _ _alpha _begin_bmatrix_ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ _hdotsfor_6_ _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _end_bmatrix]: http://docs.opencv.org/3.0-beta/_images/math/39f9e9decf02ff5891fe62d9892ff1bee82f2904.png
[alpha _ _fork_frac_1_texttt_ksize.width_ksize.height_when _texttt_normalize_true_1_otherwise]: http://docs.opencv.org/3.0-beta/_images/math/3f79f034b1b1b131f02a860eb808df10daa25525.png
[texttt_K_ _ _frac_1_texttt_ksize.width_ksize.height_ _begin_bmatrix_ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ _hdotsfor_6_ _ 1 _ 1 _ 1 _ _cdots _ 1 _ 1 _ _end_bmatrix]: http://docs.opencv.org/3.0-beta/_images/math/0a3a3decb904a04778cbe67506aa86d2dba618d6.png
[http_blog.csdn.net_yansmile1_article_details_46275791]: http://blog.csdn.net/yansmile1/article/details/46275791
[1365044411_7461.JPG]: https://img-my.csdn.net/uploads/201304/04/1365044411_7461.JPG
[Center]: /images/20220613/99437f1b7f434ee1b99b231150cceb8c.png