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 medianBlurInputArray src, OutputArray dst, int ksize

Python: cv2.medianBlursrc, 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 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:[email protected]

#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

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