Opencv图像添加椒盐噪声、高斯滤波去除噪声原理以及手写Python代码实现方法

import numpy as np
import cv2
def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
def add_sp_noise(img,sp_number):
    new_image=img
    row,col,channel=img.shape#获取行列,通道信息
    s=int(sp_number*img.size/channel)#根据sp_number确定椒盐噪声
    #确定要扫椒盐的像素值
    change=np.concatenate((np.random.randint(0,row,size=(s,1)),np.random.randint(0,col,size=(s,1))),axis=1)
    for i in range(s):
        r=np.random.randint(0,2)#确定撒椒（0）还是盐（1）
        for j in range(channel):
            new_image[change[i,0],change[i,1],j]=r
    return new_image

img=cv2.imread("C:/Users/bwy/Desktop/peppers.JPG")
im=img.copy()
im2=img.copy()
im3=img.copy()
im=add_sp_noise(im,0.05)
im2=add_sp_noise(im2,0.1)
im3=add_sp_noise(im3,0.3)
r=np.hstack((img,im,im2,im3))
cv_show('r',r)

def gaosi_f(img,k_size,sigma):
    ##滤波图片的尺寸
    h=img.shape[0]
    w=img.shape[1]
    ##用0填充边缘
    pad=k_size//2
    transform_img=np.zeros((h+2*pad,w+2*pad))
    transform_img[pad:h+pad,pad:w+pad]=img
    new_img=np.zeros((h,w))
    
    ##先计算高斯滤波核
    gaosi_filter=np.zeros((k_size,k_size))
    for x in range(-pad,-pad+k_size):
        for y in range(-pad,-pad+k_size):
            gaosi_filter[y+pad,x+pad]=np.exp(-(x**2+y**2)/(2*sigma**2))/(2*np.pi*sigma**2)
    gaosi_filter=gaosi_filter/np.sum(gaosi_filter)
    
    ##计算滤波后的图片
    for i in range(pad,h+pad):
        for j in range(pad,w+pad):
            ##取图像k_size x k_size的像素值
            p_img=transform_img[i-pad:i+pad+1,j-pad:j+pad+1]
            ##进行高斯滤波
            value=np.sum(np.multiply(p_img,gaosi_filter))
            new_img[i-pad,j-pad]=value
    ##对滤波后的图片中的像素值取整
    new_img=np.round(new_img).astype(np.uint8)
    
    return new_img

def gaosi_fS(img,k_size,sigma):
    h=img.shape[0]
    w=img.shape[1]
    imShape=img.shape
    dim=len(imShape)
    if dim==2:
        eim=gaosi_f(img,k_size,sigma)
    else:
        imR=img[:,:,0]
        imG=img[:,:,1]
        imB=img[:,:,2]
        eim=np.zeros((h,w,3))
        eimr=gaosi_f(imR,k_size,sigma)
        eimg=gaosi_f(imG,k_size,sigma)
        eimb=gaosi_f(imB,k_size,sigma)
        eim[:,:,0]=eimr
        eim[:,:,1]=eimg
        eim[:,:,2]=eimb
    return eim

new_img=gaosi_fS(im,3,0.5)
cv_show("new_img",new_img)
cv_show("im",im)

aussian = cv2.GaussianBlur(im, (5,5), 1)
cv_show("aussian",aussian)