首先是噪声的大体分类：
噪点噪声：又称脉冲噪声、椒盐噪声
雪花噪声：又称高斯噪声
条纹噪声：
细节图如下所示（图像来源，论文http://www.doc88.com/p-2572496212147.html）
分析完这些噪声的大致分布情况之后
首先需要作出这些噪声图（原型来自https://www.jb51.net/article/162073.htm）
import cv2 from PIL import Image from PIL import ImageChops import numpy as np import time import pytesseract import warnings import math import random def sp_noise(image,prob=0.05): ''' 添加椒盐噪声 prob:噪声比例 ''' output = np.zeros(image.shape,np.uint8) thres = 1 - prob for i in range(image.shape[0]): for j in range(image.shape[1]): rdn = random.random() if rdn < prob: output[i][j] = 0 elif rdn > thres: output[i][j] = 255 else: output[i][j] = image[i][j] #return output #print(output) cv2.imshow("img",output) cv2.waitKey(0) cv2.imwrite("noise_check/img.jpg",output) def gasuss_noise(image, mean=0.2, var=0.005): ''' 添加高斯噪声 mean : 均值 var : 方差 ''' image = np.array(image/255, dtype=float) noise = np.random.normal(mean, var ** 0.5, image.shape) out = image + noise if out.min() < 0: low_clip = -1. else: low_clip = 0. out = np.clip(out, low_clip, 1.0) out = np.uint8(out*255) cv2.imshow("gasuss", out) #return out cv2.waitKey(0) cv2.imwrite("noise_check/img.jpg",out) #sp_noise(cv2.imread("noise_check/5.jpg",cv2.IMREAD_COLOR)) gasuss_noise(cv2.imread("noise_check/5.jpg",cv2.IMREAD_COLOR))
检测噪点和雪花的代码如下（均方误差法，思路来源https://blog.csdn.net/twinkle_star1314/article/details/74858253）
import cv2 from PIL import Image from PIL import ImageChops import numpy as np import time import pytesseract import warnings warnings.filterwarnings("ignore") demo=Image.open("noise_check//1.jpg") im=np.array(demo.convert('L'))#灰度化矩阵 print(im.shape) print(im.dtype) #print(im) height=im.shape[0]#尺寸 width=im.shape[1] varlist=[] for i in range(height): for j in range(width): for k in range(16): if im[i][j]>=k*16 and im[i][j]<(k+1)*16:#16级量化 im[i][j]=8*(k*2+1) break for i in range(0,height-height%3,3): for j in range(0,width-width%3,3): x=(im[i][j]+im[i+1][j]+im[i+2][j]+im[i][j+1]+im[i+1][j+1]+im[i+2][j+1]+im[i][j+2]+im[i+1][j+2]+im[i+2][j+2])/9 x2=(pow(im[i][j],2)+pow(im[i+1][j],2)+pow(im[i+2][j],2)+pow(im[i][j+1],2)+pow(im[i+1][j+1],2)+pow(im[i+2][j+1],2)+pow(im[i][j+2],2)+pow(im[i+1][j+2],2)+pow(im[i+2][j+2],2))/9 var=x2-pow(x,2) varlist.append(round(var,3))#子窗口的方差值3x3 print(im) #print(varlist) T=round(sum(varlist)/len(varlist),3)#保留3位小数 print(T)
检测噪点和雪花的方法如下（FFT法）
from PIL import Image import numpy as np import math T=50#阈值设定，大于T则判定偏离xy轴过多 #复数类 class complex: def __init__(self): self.real = 0.0 self.image = 0.0 #复数乘法 def mul_ee(complex0, complex1): complex_ret = complex() complex_ret.real = complex0.real * complex1.real - complex0.image * complex1.image complex_ret.image = complex0.real * complex1.image + complex0.image * complex1.real return complex_ret #复数加法 def add_ee(complex0, complex1): complex_ret = complex() complex_ret.real = complex0.real + complex1.real complex_ret.image = complex0.image + complex1.image return complex_ret #复数减法 def sub_ee(complex0, complex1): complex_ret = complex() complex_ret.real = complex0.real - complex1.real complex_ret.image = complex0.image - complex1.image return complex_ret #对输入数据进行倒序排列 def forward_input_data(input_data, num): j = num //2 for i in range(1, num - 2): if(i < j): complex_tmp = input_data[i] input_data[i] = input_data[j] input_data[j] = complex_tmp #print "forward x[%d] <==> x[%d]" % (i, j) k = num // 2 while (j >= k): j = j - k k = k // 2 j = j + k #实现1D FFT def fft_1d(in_data, num): PI = 3.1415926 forward_input_data(in_data, num) #倒序输入数据 #计算蝶形级数，也就是迭代次数 M = 1 #num = 2^m tmp = num // 2; while (tmp != 1): M = M + 1 tmp = tmp // 2 #print "FFT level：%d" % M complex_ret = complex() for L in range(1, M + 1): B = int(math.pow(2, L -1)) #B为指数函数返回值，为float，需要转换integer for J in range(0, B): P = math.pow(2, M - L) * J for K in range(J, num, int(math.pow(2, L))): #print "L:%d B:%d, J:%d, K:%d, P:%f" % (L, B, J, K, P) complex_ret.real = math.cos((2 * PI / num) * P) complex_ret.image = -math.sin((2 * PI / num) * P) complex_mul = mul_ee(complex_ret, in_data[K + B]) complex_add = add_ee(in_data[K], complex_mul) complex_sub = sub_ee(in_data[K], complex_mul) in_data[K] = complex_add in_data[K + B] = complex_sub #print "A[%d] real: %f, image: %f" % (K, in_data[K].real, in_data[K].image) # print "A[%d] real: %f, image: %f" % (K + B, in_data[K + B].real, in_data[K + B].image) def test_fft_1d(in_data): #in_data = [2,3,4,5,7,9,10,11,100,12,14,11,56,12,67,12] #待测试的x点元素 k=1 while(1): if len(in_data)>pow(2,k) and len(in_data)<=pow(2,k+1):#不足的补0 #fftlen=pow(2,k+1) #in_data.extend([0 for i in range(pow(2,k+1)-len(in_data))]) fftlen=pow(2,k) break k+=1 #变量data为长度为x、元素为complex类实例的list，用于存储输入数据 data = [(complex()) for i in range(len(in_data))] #将8个测试点转换为complex类的形式，存储在变量data中 for i in range(len(in_data)): data[i].real = in_data[i] data[i].image = 0.0 ##输出FFT需要处理的数据 #print("The input data:") #for i in range(len(in_data)): # print("x[%d] real: %f, image: %f" % (i, data[i].real, data[i].image)) fft_1d(data, fftlen) ##输出经过FFT处理后的结果 #print("The output data:") #for i in range(len(in_data)): # print("X[%d] real: %f, image: %f" % (i, data[i].real, data[i].image)) Tnum=0 for i in range(len(in_data)):#虚实值都大于T的才叫偏离 if abs(data[i].real)>T and abs(data[i].image)>T: Tnum+=1 print(Tnum) print(str(round(Tnum/len(in_data),4)*100)+"%") #test the 1d fft #in_data=[2,3,4,5,7,9,10,11] demo=Image.open("noise_check//5.jpg") im=np.array(demo.convert('L'))#灰度化矩阵 in_data=[] for item in im: in_data.extend(item) test_fft_1d(in_data)
以下为原图、均方误差法结果、FFT法结果
可以看出FFT法比均方误差法要准确，虽然时间上也更长···
对于正常图片，这个FFT百分比一般不超过94%
对于噪声较小的也能在这个数值上体现出来：
要是更小的噪声的话···
可能准确率就不行了···
条纹噪声的检测和上述不同
不知道如何才能生成条纹噪声···
按照上面那个论文的思路倒是将代码写了出来，还未经过测试所以正确率不能保证
from PIL import Image import numpy as np import warnings T1=100#阈值1，通道行差 T2=1000#阈值2，A通道差绝对和 T3=1000#阈值3，AB通道绝对和 #算法来源，论文http://www.doc88.com/p-2572496212147.html warnings.filterwarnings("ignore") demo=Image.open("noise_check//21.jpg") im=np.array(demo.convert('L'))#灰度化矩阵 print(im.shape) print(im.dtype) r,g,b=demo.split() #gm=demo.convert('L') #plt.subplot(2,2,1) #plt.imshow(gm,cmap='gray'),plt.axis('off') #plt.subplot(2,2,2) #plt.imshow(r,cmap='gray'),plt.axis('off') #plt.subplot(2,2,3) #plt.imshow(g,cmap='gray'),plt.axis('off') #plt.subplot(2,2,4) #plt.imshow(b,cmap='gray'),plt.axis('off') #plt.show() rm=np.array(r) gm=np.array(g) bm=np.array(b) height=im.shape[0]#尺寸 width=im.shape[1] midimg=[im,rm,gm,bm] count=0 for i in range(4): mid=midimg[i] n=0 while(1): if n+3>=height:break for j in range(6,width-6,1): grA=mid[n][j] grB=mid[n+3][j] if abs(grA-grB)>T1: L1=0 L2=0 for k in range(j-6,j+6,1): L1+=abs(mid[n][k]-grA) L2+=abs(mid[n][k]-mid[n+3][k]) #print(L1) #print(L2) #print("-----") if L1<T2 and L2>T3: count+=1 n+=10 #print(count) sum=(height-3)*4*(width-12) #print(count/sum) res=round(count/sum,5)#保留3位小数 print(str(res*100)+"%")
原图是视频组播电视图，噪声图是网上找的···
可能会有用吧···
以上。
博客记录，从我做起。我是会武术之白猫，转载请注明。