把图像去噪算法用python实现
#coding:utf-8import sys,osfrom 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)] = 1def 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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