作業(yè)代碼及內(nèi)容展示和分析：

1.代碼

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):????????????????????????????? #生成椒鹽噪聲圖函數(shù)

?????? w,h = im.shape

?????? saltNoiseNum = int(w*h*var)???????????????????????????? #噪聲總的點(diǎn)數(shù)

?????? 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原圖、90%強(qiáng)度噪聲lena圖像、恢復(fù)后圖像；MSE為85.9

分析：

依據(jù)脈沖噪聲的極值準(zhǔn)則和不連續(xù)準(zhǔn)則，利用D－S證據(jù)理論進(jìn)行信息融合，在噪聲修復(fù)階段，對(duì)于檢測(cè)窗口內(nèi)非噪聲點(diǎn)的中值，利用距離矩陣和偏差矩陣進(jìn)行修正。

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總結(jié)

以上是生活随笔為你收集整理的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。的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

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