MATLAB图像增强程序

% GRAY TRANSFORM

clc;

I=imread('pout.tif');

imshow(I);

J=imadjust(I,[0.3 0.7],[0 1],1);  %transforms the walues in the %intensity image I to values in J by linealy mapping %values between 0.3 and 0.7 to values between 0 and 1.
figure;

imshow(J);

J=imadjust(I,[0.3 0.7],[0 1],0.5);  % if GAMMA is less than 1,the  mapping si weighted toward higher (brighter)

%output values.

figure;

imshow(J);

J=imadjust(I,[0.3 0.7],[0 1],1.5);  % if GAMMA is greater than 1,the mapping si weighted toward lower (darker)

%output values.

figure;

imshow(J)

J=imadjust(I,[0.3 0.7],[0 1],1);  % If TOP<BOTTOM,the output image is reversed,as in a photographic negative.

figure;

imshow(J);

 

2.直方图灰度变换

%直方图灰度变换

[X,map]=imread('forest.tif');

I=ind2gray(X,map);%把索引图像转换为灰度图像

imshow(I);

title('原图像');

improfile%用鼠标选择一条对角线，显示线段的灰度值

figure;subplot(121)

plot(0:0.01:1,sqrt(0:0.01:1))

axis square

title('平方根灰度变换函数')

subplot(122)

maxnum=double(max(max(I)));%取得二维数组最大值

J=sqrt(double(I)/maxnum);%把数据类型转换成double,然后进行平方根变换

%sqrt函数不支持uint8类型

J=uint8(J*maxnum);%把数据类型转换成uint8类型

imshow(J)

title('平方根变换后的图像')

3.直方图均衡化程序举例

% HISTGRAM EAQUALIZATION
clc;

% Clear command window
I=imread('tire.tif');

% reads the image in tire.tif into I
imshow(I);

% displays the intensity image I with 256 gray levels
figure;

%creates a new figure window
imhist(I);

% displays a histogram for the intensity image I
J=histeq(I,64);

% transforms the intensity image I,returning J an intensity
figure;

%image with 64 discrete levels
imshow(J);
figure;
imhist(J);
J=histeq(I,32);

%transforms the intensity image ,returning in % J an intensity
figure;

%image with 32 discrete levels
imshow(J);
figure;
imhist(J);

4.直方图规定化程序举例

% HISTGRAM REGULIZATION
clc;

%Clear command window
I=imread('tire.tif');

%reads the image in tire.tif into I
J=histeq(I,32);

%transforms the intensity image I,returning in
%J an intensity image with 32 discrete levels
[counts,x]=imhist(J);

%displays a histogram for the intensity image I
Q=imread('pout.tif');

%reads the image in tire.tif into I
figure;
imshow(Q);
figure;
imhist(Q);
M=histeq(Q,counts);

%transforms the intensity image Q so that the
%histogram of the output image M approximately matches counts
figure;
imshow(M);
figure;
imhist(M);

空域滤波增强部分程序

1.线性平滑滤波

I=imread('eight.tif');

J=imnoise(I,'salt & pepper',0.02);

subplot(221),imshow(I)

title('原图像')

subplot(222),imshow(J)

title('添加椒盐噪声图像')

K1=filter2(fspecial('average',3),J)/255;%应用3*3邻域窗口法

subplot(223),imshow(K1)

title('3x3窗的邻域平均滤波图像')

K2=filter2(fspecial('average',7),J)/255;%应用7*7邻域窗口法

subplot(224),imshow(K2)

title('7x7窗的邻域平均滤波图像')

 

 



2.中值滤波器
MATLAB中的二维中值滤波函数medfit2来进行图像中椒盐躁声的去除
%IMAGE NOISE REDUCTION WITH MEDIAN FILTER
clc;
hood=3;%滤波窗口
[I,map]=imread('eight.tif');
imshow(I,map);
noisy=imnoise(I,'salt & pepper',0.05);
figure;
imshow(noisy,map);
filtered1=medfilt2(noisy,[hood hood]);
figure;
imshow(filtered1,map);
hood=5;
filtered2=medfilt2(noisy,[hood hood]);
figure;
imshow(filtered2,map);
hood=7;
filtered3=medfilt2(noisy,[hood hood]);
figure;
imshow(filtered3,map);
 

3. 4邻域8邻域平均滤波算法
% IMAGE NOISE REDUCTION WITH MEAN ALGORITHM
clc;
[I,map]=imread('eight.tif');
noisy=imnoise(I,'salt & pepper',0.05);
myfilt1=[0 1 0;1 1 1;0 1 0];%4邻域平均滤波模版
myfilt1=myfilt1/9;%对模版归一化
filtered1=filter2(myfilt1,noisy);
imshow(filtered1,map);
myfilt2=[1 1 1;1 1 1;1 1 1];
myfilt2=myfilt2/9;
filtered2=filter2(myfilt2,noisy);
figure;
imshow(filtered2,map);

频域增强程序举例

1.低通滤波器
% LOWPASS FILTER
clc;
[I,map]=imread('eight.tif');
noisy=imnoise(I,'gaussian',0.05);
imshow(noisy,map);
myfilt1=[1 1 1;1 1 1;1 1 1];
myfilt1=myfilt1/9;
filtered1=filter2(myfilt1,noisy);
figure;
imshow(filtered1,map);
myfilt2=[1 1 1;1 2 1;1 1 1];
myfilt2=myfilt2/10;
filtered2=filter2(myfilt2,noisy);
figure;
imshow(filtered2,map);
myfilt3=[1 2 1;2 4 2; 1 2 1];
myfilt3=filter2(myfilt3,noisy);
figure;
imshow(filtered3,map);
 

2.布特沃斯低通滤波器图像实例
I=imread('saturn.png');

J=imnoise(I,'salt & pepper',0.02);

subplot(121),imshow(J)

title('含噪声的原图像')

J=double(J);

f=fft2(J);

g=fftshift(f);

[M,N]=size(f);

n=3;d0=20;

n1=floor(M/2);n2=floor(N/2);

for i=1:M;

for j=1:N;

d=sqrt((i-n1)^2+(j-n2)^2);

h=1/(1+0.414*(d/d0)^(2*n));

g(i,j)=h*g(i,j);

end

end

g=ifftshift(g);

g=uint8(real(ifft2(g)));

subplot(122),imshow(g)

title('三阶Butterworth滤波图像')



 

色彩增强程序举例

1.真彩色增强实例：
%真彩色图像的分解

clc;

RGB=imread('peppers.png');

subplot(221),imshow(RGB)

title('原始真彩色图像')

subplot(222),imshow(RGB(:,:,1))

title('真彩色图像的红色分量')

subplot(223),imshow(RGB(:,:,2))

title('真彩色图像的绿色分量')

subplot(224),imshow(RGB(:,:,3))

title('真彩色图像的蓝色分量')



 
2.伪彩色增强举例：
I=imread('cameraman.tif');

imshow(I);

X=grayslice(I,16);%thresholds the intensity image I using

%threshold values 1/16,2/16,…..,15/16,returning an indexed %image in X

figure;

imshow(X,hot(16));
 


 
3.假彩色增强处理程序举例

[RGB]=imread('ghost.bmp');

imshow(RGB);

RGBnew(:,:,1)=RGB(:,:,3);

RGBnew(:,:,2)=RGB(:,:,1);

RGBnew(:,:,3)=RGB(:,:,2);

figure;

subplot(121);

imshow(RGB);

subplot(122);

imshow(RGBnew);