均值滤波

 

即　对该点附近的　一般为３×３或者５×５矩阵求和，然后取求均值放回该点

 

例如

 

12  23  67   34  222   52    16    79
52  25  97    94  202  42    96    39
82  64  227  84  22    20    126  59
42  27  123  54  212  232  156  249
22  23  97    94  42    132  186  179

...........

均值处理：

以２２７值的点为例子：

 

 

对２２７值的点附近３×３矩阵的点求和-->除以９，即求均值

 

ave=(15+97+94+64+227+84+27+123+54)／9
     =785／９

     =87.22

均值为87.22

将该值87放回原来227值的点的位置

 

 

其他各点用同样的方法求均值

 

◆ blur()
void cv::blur     (     InputArray      src,
        OutputArray      dst,
        Size      ksize,
        Point      anchor = Point(-1,-1),
        int      borderType = BORDER_DEFAULT
    )         
Python:
    dst    =    cv.blur(    src, ksize[, dst[, anchor[, borderType]]]    )

#include <opencv2/imgproc.hpp>

Blurs an image using the normalized box filter.

The function smooths an image using the kernel:

 

 

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

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, see BorderTypes. BORDER_WRAP is not supported.

See also
    boxFilter, bilateralFilter, GaussianBlur, medianBlur

Examples:
    samples/cpp/edge.cpp, samples/cpp/laplace.cpp, and samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp.

 

 

参考链接：

https://docs.opencv.org/master/d4/d86/group__imgproc__filter.html#ga8c45db9afe636703801b0b2e440fce37