数学之路-python计算实战(15)-机器视觉-滤波去噪(归一化块滤波)

浏览数：204 / 时间：2015年06月08日

# -*- coding: utf-8 -*-   
#code:[email protected]
#归一化块滤波
import cv2
import numpy as np
fn="test3.jpg"
myimg=cv2.imread(fn)
img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY)

#加上高斯噪声，能够參考曾经博文中的内容
......
......
#滤波去噪
lbimg=cv2.blur(newimg,(3,3))
cv2.imshow(‘src‘,newimg)
cv2.imshow(‘dst‘,lbimg)
cv2.waitKey()
cv2.destroyAllWindows()

右图是加上噪声，左图是去除噪声后，尽管进行了图像模糊，但仍能比較清晰

依据原理，使用第3个脉冲响应函数（也有人称它为核函数），例如以下:

本博客全部内容是原创，假设转载请注明来源

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

用python实现这个算法

#code:[email protected]
#归一化块滤波
...
...
#用第3个脉冲响应函数
a=1/16.0
kernel=a*np.array([[1,2,1],[2,4,2],[1,2,1]])
for y in xrange(1,myh-1):
    for x in xrange(1,myw-1):
        lbimg[y,x]=np.sum(kernel*tmpimg[y-1:y+2,x-1:x+2])
    print ".",

效果例如以下图

opencv提供的blur函数使用说明例如以下：

Blurs an image using the normalized box filter.

C++: void blur(InputArray src, OutputArray dst, Size ksize, Pointanchor=Point(-1,-1), int borderType=BORDER_DEFAULT )

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

Parameters:

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.

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.

注意，blur函数使用了第1个脉冲响应函数，例如以下：

The function smoothes an image using the kernel:

对椒盐噪声的归一化块滤波滤波，须要将作用域扩大，但会更模糊，但效果更好

# -*- coding: utf-8 -*-   
#code:[email protected]
#归一化块滤波
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=100000
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.blur(newimg,(5,5))
cv2.imshow(‘src‘,newimg)
cv2.imshow(‘dst‘,lbimg)
cv2.waitKey()
cv2.destroyAllWindows()