Experiment 2 image enhancement

One 、 The experiment purpose ：
（1） Further master image processing tools Matlab, Be familiar with based on Matlab Image processing function .
（2） Master various image enhancement methods .
Two 、 Experimental principle ( A little ）
3、 ... and 、 The experimental steps （ Including analysis 、 Code and waveform ）
First, let's take a look at the content of this experiment .
1. Open a color image Image1, Use Matlab Image processing functions , Make the following transformations ：
（1） take Image1 Grayscale to gray, Statistics and display its gray histogram ;
（2） Yes gray Carry out piecewise linear transformation ;
（3） Yes gray Histogram equalization ;
（4） Yes gray Pseudo color enhancement ;
（5） Yes gray Add noise and smooth ;
（6） Yes gray utilize Sobel Operator sharpening ;
（7） Expanded content in experimental requirements .

The idea of the experiment is very clear , Combined with the discussion in the principle , We just need to follow the requirements of the title and the reference code 、 Just check the operation and verify it , There is no need to calculate .

** Here's number one (1) Code of topic .** This part will Image1 Grayscale to gray, Statistics and display its gray histogram ;

Image1=im2double(imread('fj.jpg'));
gray=rgb2gray(Image1);
imhist(gray); 
[h,w]=size(gray);                                     
NewImage1=zeros(h,w);                 
a=80/256; b=180/256; c=30/256; d=220/256;
for x=1:w
    for y=1:h
        if gray(y,x)<a 
            NewImage1(y,x)=gray(y,x)*c/a;
        elseif gray(y,x)<b
            NewImage1(y,x)=(gray(y,x)-a)*(d-c)/(b-a)+c;
        else
            NewImage1(y,x)=(gray(y,x)-b)*(255-d)/(255-b)+d;
        end                                               
    end
end
figure,imshow(NewImage1),title(' Piecewise linear transformation ');

The gray histogram obtained is shown in the figure below ：

** Here's number one (2) Code of topic .** Yes gray Carry out piecewise linear transformation

Image1=im2double(imread('fj.jpg'));
gray=rgb2gray(Image1);
imhist(gray); 
[h,w]=size(gray);                                     
NewImage1=zeros(h,w);                 
a=80/256; b=180/256; c=30/256; d=220/256;
for x=1:w
    for y=1:h
        if gray(y,x)<a 
            NewImage1(y,x)=gray(y,x)*c/a;
        elseif gray(y,x)<b
            NewImage1(y,x)=(gray(y,x)-a)*(d-c)/(b-a)+c;
        else
            NewImage1(y,x)=(gray(y,x)-b)*(255-d)/(255-b)+d;
        end                                               
    end
end
figure,imshow(NewImage1),title(' Piecewise linear transformation ');

The obtained piecewise linear transformation is shown in the figure below ：

** Here's number one (3) Code of topic .** Yes gray Histogram equalization ;

NewImage2=histeq(gray);
figure,imshow(NewImage2),title(' Histogram equalization ');

The histogram equalization diagram obtained is as follows ：

** Here's number one (4) Code of topic .** Yes gray Pseudo color enhancement

NewImage3=zeros(h,w,3);
for x=1:w
    for y=1:h
        if gray(y,x)<64/256
            NewImage3(y,x,1)=0;
            NewImage3(y,x,2)=4*gray(y,x);
            NewImage3(y,x,3)=1;
        elseif gray(y,x)<128/256
            NewImage3(y,x,1)=0;
            NewImage3(y,x,2)=1;
            NewImage3(y,x,3)=2-4*gray(y,x);
        elseif gray(y,x)<192/256
            NewImage3(y,x,1)=4*gray(y,x)-2;
            NewImage3(y,x,2)=1;
            NewImage3(y,x,3)=0;
        else
            NewImage3(y,x,1)=1;
            NewImage3(y,x,2)=4-4*gray(y,x);
            NewImage3(y,x,3)=0;
        end
    end
end
figure,imshow(NewImage3);

The image of pseudo color enhancement is as follows ：

** Here's number one (5) Code of topic .** Yes gray Add noise and smooth

noiseIsp=imnoise(gray,'salt & pepper',0.1);            
noiseIg=imnoise(gray,'gaussian');  
result1=medfilt2(noiseIsp); 
result2=medfilt2(noiseIg);
figure;
subplot(121),imshow(result1),title(' Salt and pepper noise 3×3 median filtering ');
subplot(122),imshow(result2),title(' Gaussian noise 3×3 median filtering ');

The figure with noise added and smoothed is as follows ：

Here's number one (6) Code of topic . The ministry requires that gray utilize Sobel Operator sharpening

H1=[-1 -2 -1;0 0 0;1 2 1]; 
H2=[-1 0 1;-2 0 2;-1 0 1];  
R1=imfilter(gray,H1);    
R2=imfilter(gray,H2);
edgeImage=abs(R1)+abs(R2);    
sharpImage=gray+edgeImage;               
figure;
subplot(121),imshow(edgeImage),title('Sobel Gradient image ');             
subplot(122),imshow(sharpImage),title('Sobel sharpen image ');

obtain Sobel The sharpened graph of the operator is as follows ：

2. Expanded content in experimental requirements
（1） Transform the parameters for the above processing , Check the processing effect ;
（2） Change the pseudo color enhancement method to hot metal coding or rainbow coding ;
（3） Design different smoothing filters 、 Sharpening filtering method , Check the processing effect ;
（4） Self design method , Realize the color image enhancement processing .
The following is the expansion (1) Code of topic . Processing transformation parameters , Check the processing effect
（ A little , Refer to the previous part of the code , Because of too much , Don't put it up again ）

The figure of transformation parameters is as follows ：

The following is the expansion （2） Code of topic . This topic requires changing the pseudo color enhancement method to hot metal coding or rainbow coding .

Rainbow code ：

Image1=imread('lotus.bmp');
    
% Convert to grayscale 
gray=rgb2gray(Image1);
[h,w]=size(gray);
% Matrix of the new image 
NewImage3=zeros(h,w,3);
for x=1:h
    for y=1:w
        if gray(x,y)<96
        NewImage3(x,y,1)=0;
        elseif gray(x,y)<128
        NewImage3(x,y,1)=255*(gray(x,y)-96)/32;
        else
        NewImage3(x,y,1)=255;
        end
    end
end
for x=1:h
    for y=1:w
        if gray(x,y)<32
        NewImage3(x,y,2)=0;
        elseif gray(x,y)<64
        NewImage3(x,y,2)=255*(gray(x,y)-32)/32;
        elseif gray(x,y)<128
        NewImage3(x,y,2)=255;
        elseif gray(x,y)<192
        NewImage3(x,y,2)=255*(192-gray(x,y))/64;
        else
        NewImage3(x,y,2)=255*(gray(x,y)-192)/64;
        end
    end
end
for x=1:h
    for y=1:w
        if gray(x,y)<32
        NewImage3(x,y,3)=255*gray(x,y)/32;
        elseif gray(x,y)<64
        NewImage3(x,y,3)=255;
        elseif gray(x,y)<96
        NewImage3(x,y,3)=255*(96-gray(x,y))/32;
        elseif gray(x,y)<192 
        NewImage3(x,y,3)=0;
        else
        NewImage3(x,y,3)=255*(gray(x,y)-192)/64;
        end
    end
end
imshow(NewImage3),title(' Rainbow code ')

The figure of color image transformation to rainbow coding is as follows ：

The following is the expansion （3） Code of topic . This topic designs different smoothing filters 、 Sharpening filtering method , Check the processing effect .
1. be based on Robert Cross gradient image sharpening ：

I = imread('fj.jpg');  
imshow(I);   
I = double(I); %  Convert to double type , This saves negative values , otherwise uint8 Type will cut off the negative value   
w1 = [-1 0; 0 1]  
w2 = [0 -1; 1 0]  
G1 =  imfilter(I, w1, 'corr', 'replicate'); %  Fill the boundary repeatedly   
G2 =  imfilter(I, w2, 'corr', 'replicate');  
G = abs(G1) + abs(G2); %  Calculation Robert gradient    
figure, imshow(G, []);   
figure, imshow(abs(G1), []);  
figure, imshow(abs(G2), []);  
2.  Smoothing filter code 
I = imread('fj.jpg');
J1=imnoise(I,'salt & pepper',0.02); % Plus mean value is 0, The variance of 0.02 Salt and pepper noise 
J2=imnoise(I,'gaussian',0.02); % Plus mean value is 0, The variance of 0.02 Gaussian noise of .
g1=rgb2gray(J1);
g2=rgb2gray(J2);
figure('units','normalized','position',[0 0 1 1]);
subplot(2,4,1),imshow(J1),xlabel(' Salt and pepper noise '); % Displays a salt and pepper noise image 
subplot(2,4,2),imshow(J2),xlabel(' Gaussian noise '); % Gaussian noise image is displayed 

% %   Neighborhood averaging   neighborhood averaging

% imfilter  It can carry out multi-dimensional image （RGB etc. ） Spatial filtering , And there are many optional parameters 
% filter2 / medfilt2  Only two-dimensional images （ grayscale ） Spatial filtering ,
%  The result types of the two functions are different , Only need I1=filter2(h,I) Followed by I1=uint8(I1) Do type conversion , The result is the same .

K1 = filter2(fspecial('average',3),g1); %  Salt and pepper noise  3*3  Template smoothing filtering 
K2 = filter2(fspecial('average',11),g1);
k3 = imfilter(I,fspecial('average',3),'replicate');
K4 = filter2(fspecial('average',3),g2); %  Gaussian noise  3*3  Template smoothing filtering 

subplot(2,4,3),imshow(uint8(K1)),xlabel({
    ' Salt and pepper noise ';'3*3  Template smoothing filtering '});
subplot(2,4,4),imshow(uint8(K1)),xlabel({
    ' Salt and pepper noise ';'11*11  Template smoothing filtering '});
subplot(2,4,5),imshow(k3),xlabel('3*3 imfilter Spatial filtering ');
subplot(2,4,6),imshow(uint8(K4)),xlabel(' Gaussian noise  3*3  Template smoothing filtering ');

%  median filtering 
I1= medfilt2(g1,[3,3]); %  The image with salt and pepper noise is 5×5 Square window median filter 
I2= medfilt2(g2,[3,3]); %  For images with Gaussian noise 5×5 Square window median filter 

subplot(2,4,7),imshow(I1),xlabel({
    ' Salt and pepper noise ';'3*3  median filtering '}); 
subplot(2,4,8),imshow(I2),xlabel({
    ' Gaussian noise ';'3*3  median filtering '}); 

Get self-designed based on smooth filtering 、 The figure of sharpened filtered image is as follows ：

( Sharpening filter )

（ Smooth filtering ）

** The following is the expansion （4） Code of topic .** Self design method , Realize the color image enhancement processing .
Yes RGB Image direct linear transformation for color enhancement

clear;clc;
rgb  = imread('fj.jpg');  
[o p q]=size(rgb);
r(:,:,1)=rgb(:,:,1);
r(:,:,2)=zeros(o,p); 
r(:,:,3)=zeros(o,p);
r=r*1.4;
g(:,:,2)=rgb(:,:,2);
g(:,:,1)=zeros(o,p); 
g(:,:,3)=zeros(o,p);
g=g*1.4;
b(:,:,3)=rgb(:,:,3);
b(:,:,2)=zeros(o,p); 
b(:,:,1)=zeros(o,p);
b=b*1.4;
rgb_new(:,:,1)=r(:,:,1); 
rgb_new(:,:,2)=g(:,:,2);
rgb_new(:,:,3)=b(:,:,3);
figure;
subplot(121);imshow(rgb);title(' original rgb Images ');  
%subplot(231);imshow(r);title(' Red component image ');
%subplot(232);imshow(g);title(' Green component image ');
%subplot(233);imshow(b);title(' Blue component image ');
subplot(122);imshow(rgb_new);title(' After enhancement rgb Images ');

The picture of color image enhancement is as follows ：

Four 、 Summary of the experiment
Further master image processing tools Matlab, Be familiar with based on Matlab Image processing function . Through experiments, I have a deep understanding of various image enhancement methods , For example, master will Image1 Grayscale to gray, Statistics and display its gray histogram ; Yes gray Carry out piecewise linear transformation ; Yes gray Histogram equalization ; Yes gray Pseudo color enhancement ; Yes gray Add noise and smooth ; Yes gray utilize Sobel Operator sharpening method and so on , It lays a foundation for further study .