现代数字图像处理作业—对lena.bmp图像转成灰度图后，加入90%强度的椒盐噪声。尝试任何方法去恢复图像。

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作业代码及内容展示和分析：

1.代码

import cv2 as cv

import random as rd

import matplotlib.pyplot as plt

import numpy as np

le = cv.imread(‘lena.jpg’,0)

lena = le.copy()

def saltNoise_image(im,var):                              #生成椒盐噪声图函数

       w,h = im.shape

       saltNoiseNum = int(w*h*var)                             #噪声总的点数

       all_xy_pos = [[[i,j] for j in range(h)]for i in range(w)]

       xy = []

       for i in all_xy_pos:

              xy.extend(i)

       salt_xy = rd.sample(xy,saltNoiseNum)

       for i,j in salt_xy:

              ra = rd.choice([0,1])

              if ra == 0:

                     im[i,j] = 0

              else:

                     im[i,j] = 255

       return im

def border_image(im):

       im[im==255] = 0

       h,w = im.shape

       im_mask = np.zeros((h+8,w+8),dtype=np.uint8)

       im_mask[4:h+4,4:w+4] = im

       return im_mask

def get_median(arr):

       x = arr[arr!=0]

       x.sort()

       half = len(x)//2

       if len(x)%2 == 0:

              return x[half-1]//2 + x[half]//2

       else:

              return x[half]

def coreMat_cmt(a,b,im):

       arr = im[a-1:a+2,b-1:b+2]

       if arr.sum() == 0:

              arr = im[a-2:a+3,b-2:b+3]

              if arr.sum() == 0:

                     arr = im[a-3:a+4,b-3:b+4]

                     if arr.sum() == 0:

                            arr = im[a-4:a+5,b-4:b+5]

                            if arr.sum() == 0:

                                   return 0

                            else:

                                   gray = get_median(arr)

                                   return gray

                     else:

                            gray = get_median(arr)

                            return gray

              else:

                     gray = get_median(arr)

                     return gray

       else:

              gray = get_median(arr)

              return gray

def run_filter(im):

       h,w = im.shape

       im_mask = np.zeros((h-8,w-8),dtype=np.uint8)

       for i in range(4,h-4):

              for j in range(4,w-4):

                     if im[i,j] ==0:

                            new_gray = coreMat_cmt(i,j,im)

                            im_mask[i-4,j-4] = new_gray

                     else:

                            im_mask[i-4,j-4] = im[i,j]

       return im_mask

def filter_image(im):

       for i in range(5):

              border_im = border_image(im)

              im = run_filter(border_im)

       return im

noise_lena = saltNoise_image(lena,0.9)

filter_lena = filter_image(noise_lena)

cv.imshow(‘0’,le)

cv.imshow(‘1’,noise_lena)

cv.imshow(‘2’,filter_lena)

x = 0

for i in range(lena.shape[0]):

       for j in range(lena.shape[1]):

              x += abs(int(le[i,j]) – int(filter_lena[i,j]))

print(x/(400*400))

if cv.waitKey() == ord(‘A’):

       cv.destroyAllWindows()

2.lena原图、90%强度噪声lena图像、恢复后图像；MSE为85.9

分析：

依据脉冲噪声的极值准则和不连续准则，利用D－S证据理论进行信息融合，在噪声修复阶段，对于检测窗口内非噪声点的中值，利用距离矩阵和偏差矩阵进行修正。