模拟添加高斯椒盐噪声

添加高斯噪声

import cv2 import numpy as np #coutn是噪声的个数 fn = "123.jpg" if __name__ == '__main__': print('load %s ...' % fn) img = cv2.imread(fn) coutn = 10000 for k in range(0,coutn): #get the random point xi = int(np.random.uniform(0,img.shape[1])) xj = int(np.random.uniform(0,img.shape[0])) #判断2D 或 3D图 if img.ndim == 2: img[xj,xi] = 255 elif img.ndim == 3: img[xj,xi,0] = 255 #B img[xj,xi,1] = 255 #G img[xj,xi,2] = 255 #R cv2.namedWindow('img',0) cv2.resizeWindow("img", 550, 980) cv2.imshow('img',img) cv2.imwrite("gsblack.jpg",img) cv2.waitKey(0) cv2.destroyAllWindows()

添加椒盐噪声

#对添加了Gaussian和Salt噪声的图像进行恢复，前期的任务是生成噪声污染的图像。 #噪声图像的生成采用对图像进行高斯平滑，之后在随机的对图像添加椒盐噪声。 #用到的方法： #random.random_integers:产生范围内的随机整数 #cv2.GaussianBlur:对图像进行高斯滤波 import cv2 from numpy import * def SaltAndPepper(src,percetage): NoiseImg=src NoiseNum=int(percetage*src.shape[0]*src.shape[1]) for i in range(NoiseNum): randX=random.random_integers(0,src.shape[0]-1) randY=random.random_integers(0,src.shape[1]-1) if random.random_integers(0,1)==0: NoiseImg[randX,randY]=0 else: NoiseImg[randX,randY]=255 return NoiseImg if __name__=='__main__': img=cv2.imread('123.png') #对图像进行高斯滤波 gimg=cv2.GaussianBlur(img,(7,7),sigmaX=1) NoiseImg=SaltAndPepper(gimg,0.02) #cv2.imshow('img',gimg) #figure() ################################################################# Pers=[0.001,0.002,0.003] for i in Pers: NoiseImg=SaltAndPepper(gimg,i) fileName='GaussianSaltPepper'+str(i)+'.jpg' cv2.imwrite(fileName,NoiseImg,[cv2.IMWRITE_JPEG_QUALITY,100]) cv2.namedWindow('img',0) cv2.resizeWindow("img", 550, 980) cv2.imshow('img',NoiseImg) cv2.waitKey()