数字图像处理实验（⼀）（邻域平均法滤波、中值滤波等）
数字图像处理实验（⼀）（邻域平均法、中值滤波等）
内容摘要
最近，做数字图像处理实验，简单记录⼀下⼏个挺有意思的图像处理⽅法。本篇主要介绍领域平均法和中值滤波法去除图像噪声，求图像SNR和PSNR及其matlab实现代码。一语定乾坤
邻域平均法编导生
简单来说，就是对于每⼀个像素点都取它周围⼀圈的平均值，来降低⼀些像素值突变的点（即噪声点）的影响。当然，⼀个明显的结果就是，图⽚会⼀定程度上变模糊，于是有⼈提出“超限领域平均法”，即如果某个像素的灰度值⼤于其邻域像素的平均值，且达到⼀定⽔平，则判断该像素为噪声，继⽽⽤邻域像素的均值取代这⼀像素值。
代码实现如下，使⽤⽅法参见主程序注释。
%邻域均值法去燥
function Img_average_filter=Average_filter(varargin)
narginchk(1,3);
Img_in = varargin{1};
Temp =[111;111;111];%默认模板
Threshold =0;%默认阈值
if nargin ==2
Temp = varargin{2};
elif nargin ==3
Temp = varargin{2};
Threshold = varargin{3};
end
[n, m]=size(Img_in);
Img_raw =int32(Img_in);
[L1,L2]=size(Temp);
LL=sum(sum(Temp));
Img_average_filter =int32(zeros(n-L1+1, m-L2+1));%舍弃边缘信息
for i =(L1+1)/2:n-(L1-1)/2
for j =(L2+1)/2:m-(L2-1)/2
Img_raw_sub =Img_raw(i-(L1-1)/2:i+(L1-1)/2, j-(L2-1)/2:j+(L2-1)/2);
SUM=sum(sum(Img_raw_sub.*int32(Temp)));
ave =SUM/LL;
if Threshold ~=0
if abs(Img_raw(i,j)-ave)> Threshold
Img_average_filter(i-(L1-1)/2, j-(L2-1)/2)= ave;
el
Img_average_filter(i-(L1-1)/2, j-(L2-1)/2)=Img_raw(i,j);
end
el
Img_average_filter(i-(L1-1)/2, j-(L2-1)/2)= ave;
end
end真空蒸馏
end
中值滤波
简单来说，就是对于每⼀个像素点，取其周围像素值的中值，来代替该像素点的灰度值，当然，同样也可以加上⼀个阈值条件。
代码实现如下，使⽤⽅法参见主程序注释。
%中值滤波法去燥
function Img_med_filter=Med_filter(varargin)
narginchk(1,3);
Img_in = varargin{1};
L=3;%默认滑动窗⼝尺⼨
Threshold =0;%默认阈值
if nargin ==2
L= varargin{2};
elif nargin ==3
L= varargin{2};邓伦电视剧
Threshold = varargin{3};
end
[n, m]=size(Img_in);
Img_raw =int32(Img_in);
Img_med_filter =int32(zeros(n-L+1, m-L+1));%弃边缘信息
for i =(L+1)/2:n-(L-1)/2
for j =(L+1)/2:m-(L-1)/2
Img_raw_sub =reshape(Img_raw(i-(L-1)/2:i+(L-1)/2, j-(L-1)/2:j+(L-1)/2),1,L*L);
Img_raw_sub =sort(Img_raw_sub);
ave =Img_raw_sub((L*L+1)/2);
if Threshold ~=0
if abs(Img_raw(i,j)-ave)> Threshold
Img_med_filter(i-(L-1)/2, j-(L-1)/2)= ave;
el古诗回乡偶书
Img_med_filter(i-(L-1)/2,j-(L-1)/2)=Img_raw(i,j);
end
el
Img_med_filter(i-(L-1)/2, j-(L-1)/2)= ave;
end
end
end
求信噪⽐SNR和峰值信噪⽐PSNR
function SNR(Img_raw, Img_noi, Img_filter)
Img_raw_signal =double(0);
Img_noi_signal =double(0);
Img_filter_signal =double(0);
[n0, m0]=size(Img_raw);
[n1, m1]=size(Img_filter);
stx =(n0-n1)/2+1; edx = n0-stx+1;
sty =(m0-m1)/2+1; edy = m1-sty+1;
for i = stx:edx
for j = sty:edy
Img_raw_signal = Img_raw_signal+Img_raw(i,j)*Img_raw(i,j);
Img_noi_signal = Img_noi_signal+(Img_raw(i,j)-Img_noi(i,j))*(Img_raw(i,j)-Img_noi(i,j));
Img_filter_signal = Img_filter_signal+(Img_raw(i,j)-Img_filter(i-stx+1,j-sty+1))*(Img_raw(i,j)-Img_filter(i-stx+1,j-sty+1)); end
end
Img_raw_snr =10.0*log10(double(Img_raw_signal/Img_noi_signal));
Img_raw_psnr =10.0*log10(double(n1*m1*255.0*255.0/Img_noi_signal));
Improved_snr =10.0*log10(double(Img_noi_signal/Img_filter_signal));
Img_filter_snr =10.0*log10(double(Img_raw_signal/Img_filter_signal));
Img_filter_psnr =10.0*log10(double(n1*m1*255.0*255.0/Img_filter_signal));
disp('Input image snr = ');
disp(Img_raw_snr);
蓦然怎么读disp('Input image psnr = ');
disp(Img_raw_psnr);
disp('Improved snr = ');
disp(Improved_snr);
disp('Output image snr = ');
disp(Img_filter_snr);
disp('Output image psnr = ');
disp(Img_filter_psnr);
主程序⼊⼝
% ex_main.m 主程序⼊⼝
clc,clear;
%原始图像处理
Img_raw =imread('lena.jpg');
figure(1);
[n, m, c]=size(Img_raw);
if c ~=1
Img_raw =rgb2gray(Img_raw);
end
subplot(2,2,1);
imshow(Img_raw);
title('原始灰度图像');
%加噪声
%加‘salt & pepper’噪声
Img_noi =imnoi(Img_raw,'salt & pepper',0.03);
%加‘speckle’噪声
% Img_noi =imnoi(Img_raw,'speckle');
% Img_noi = Img_raw;
subplot(2,2,2);
imshow(Img_noi);
title('原始噪声图像');
%可选邻域均值法常⽤模板
% Temp=[111;111;111];
% Temp=[111;121;111];
% Temp=[121;242;121];
% Temp=[111;101;111];
Temp=[11111;11111;11111;11111;11111];
%邻域平均法去噪声
%Average_filter(图⽚像素矩阵，模板，阈值)
%⽤法：
%Average_filter(Img_noi);
%Average_filter(Img_noi, Temp);
%Average_filter(Img_noi, Temp,35);
%含有阈值T，即为阈值为T的超限领域平均法
Img_average_filter=Average_filter(Img_noi, Temp,35); subplot(2,2,3);
产品标识标注规定imshow(uint8(Img_average_filter));
title('邻域平均法去噪声');
disp('Average_filter');
%求SNR和PSNR
SNR(int32(Img_raw),int32(Img_noi),int32(Img_average_filter));
%中值滤波法去噪声
%Med_filter(图⽚像素矩阵，滑动窗⼝尺⼨，阈值)
%⽤法：
%Med_filter(Img_noi);
%Med_filter(Img_noi,3);
%Med_filter(Img_noi,3,35);
Img_med_filter=Med_filter(Img_noi);
subplot(2,2,4);
imshow(uint8(Img_med_filter));
title('中值滤波法去噪声');
disp('Med_filter');
形容仔细的成语%求SNR和PSNR
%SNR(原始图像，噪声图像，滤波后图像)
SNR(int32(Img_raw),int32(Img_noi),int32(Img_med_filter));
Lena图⽚
