把图像去噪算法用python实现
#coding:utf-8import sys,os
from PIL import Image,ImageDraw
#二值数组
t2val = {}
def twoValue(image,G):
for y in xrange(0,image.size):
for x in xrange(0,image.size):
g = image.getpixel((x,y))
if g > G:
t2val[(x,y)] = 1
else:
t2val[(x,y)] = 0
# 降噪
# 根据一个点A的RGB值，与周围的8个点的RBG值比较，设定一个值N（0 <N <8），当A的RGB值与周围8个点的RGB相等数小于N时，此点为噪点
# G: Integer 图像二值化阀值
# N: Integer 降噪率 0 <N <8
# Z: Integer 降噪次数
# 输出
#0：降噪成功
#1：降噪失败
def clearNoise(image,N,Z):
for i in xrange(0,Z):
t2val[(0,0)] = 1
t2val[(image.size - 1,image.size - 1)] = 1
for x in xrange(1,image.size - 1):
for y in xrange(1,image.size - 1):
nearDots = 0
L = t2val[(x,y)]
if L == t2val[(x - 1,y - 1)]:
nearDots += 1
if L == t2val[(x - 1,y)]:
nearDots += 1
if L == t2val[(x- 1,y + 1)]:
nearDots += 1
if L == t2val[(x,y - 1)]:
nearDots += 1
if L == t2val[(x,y + 1)]:
nearDots += 1
if L == t2val[(x + 1,y - 1)]:
nearDots += 1
if L == t2val[(x + 1,y)]:
nearDots += 1
if L == t2val[(x + 1,y + 1)]:
nearDots += 1
if nearDots < N:
t2val[(x,y)] = 1
def saveImage(filename,size):
image = Image.new("1",size)
draw = ImageDraw.Draw(image)
for x in xrange(0,size):
for y in xrange(0,size):
draw.point((x,y),t2val[(x,y)])
image.save(filename)
image = Image.open("d:/1.jpg").convert("L")
twoValue(image,100)
clearNoise(image,4,1)
saveImage("d:/5.jpg",image.size)
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