数学之路-python计算实战(17)-机器视觉-滤波去噪(中值滤波)

Blurs an image using the median filter.

C++: void medianBlur(InputArray src, OutputArray dst, int ksize)

Python: cv2.medianBlur(src, ksize[, dst]) → dst

Parameters:	src – input 1-, 3-, or 4-channel image; when `ksize` is 3 or 5, the image depth should be `CV_8U`, `CV_16U`, or `CV_32F`, for larger aperture sizes, it can only be `CV_8U`. dst – destination array of the same size and type as `src`. ksize – aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...

The function smoothes an image using the median filter with the $exttt{ksize} imes exttt{ksize}$ aperture. Each channel of a multi-channel image is processed independently. In-place operation is supported.

中值滤波将图像的每一个像素用邻域 (以当前像素为中心的正方形区域)像素的中值取代。

与邻域平均法相似，但计算的是中值

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http://blog.csdn.net/myhaspl/

#用中值法
for y in xrange(1,myh-1):
    for x in xrange(1,myw-1):
        lbimg[y,x]=np.median(tmpimg[y-1:y+2,x-1:x+2]

以下是调用opencv 的函数

# -*- coding: utf-8 -*-   
#code:myhaspl@myhaspl.com
#中值滤波
import cv2
import numpy as np
fn="test3.jpg"
myimg=cv2.imread(fn)
img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY)

#加上椒盐噪声
#灰阶范围
w=img.shape[1]
h=img.shape[0]
newimg=np.array(img)
#噪声点数量
noisecount=50000
for k in xrange(0,noisecount):
    xi=int(np.random.uniform(0,newimg.shape[1]))
    xj=int(np.random.uniform(0,newimg.shape[0]))
    newimg[xj,xi]=255


#滤波去噪
lbimg=cv2.medianBlur(newimg,3)
cv2.imshow('src',newimg)
cv2.imshow('dst',lbimg)
cv2.waitKey()
cv2.destroyAllWindows()

中值滤波忽略了较高阶灰度和较低阶灰度，直接取中值，由于有效得过滤椒盐噪声

对高斯噪声的滤波