Image resize
img1=imread('C:\Users\vignan\Documents\221fa04719\tiger.jpg'); 
img2=imresize(img1, size(100,100)); 
imshow(img2);

Arithematic operations
image1= imread("bts7.jpeg");
image2=imread("bts8.jpeg");
addsize= imresize(image2,[size(image1,1),size(image1,2)]);
add= image1 + addsize;
imshow(add);
subtract= image1 - addsize;
imshow(subtract);
mul= image1 .* addsize;
imshow(mul);
div= image1 ./ addsize;
imshow(div);

Operations
img1=imread('C:\Users\vignan\Downloads\squarrell.jpg');
img2=imread('C:\Users\vignan\Downloads\flower.jpg');
img2=imresize(img1,[size(100,100)]);
img1=im2bw(img1);
img2=im2bw(img2);
img3=img1  & img2;
img4=img1  | img2;
img5=xor(img1,img2);
img6=~img1;
subplot(141),imshow(img3),title('logical and');
subplot(142),imshow(img4),title('logical or');
subplot(143),imshow(img5),title('logical xor');
subplot(144),imshow(img6),title('logical not');

Negative image
skl=imread('C:\Users\vignan\Downloads\flowers1.jpg');
subplot(1,2,1);
imshow(skl);
title('Original image');
L=2^8;
neg=(L-1)-skl;
subplot(1,2,2);
imshow(neg);
title('Negative Image');

Log transformation
img=imread('C:\Users\vignan\Downloads\prabhas photos 13.jpg');
r=double(img); 	
C=1;
S=C*log(1+r);
T=255/(C*log(256));
B=uint8(T*S);
figure,imshow(B);
title('Log Transformation');

Power log transformation
img=imread('C:\Users\vignan\Downloads\mickey.jpg');
r=double(img);
G=3;
C=1;
S=C*(r.^G);
T=255/(C*(255.^G));
O=uint8(T.*S);
figure,
imshow(O);
title('Power Law Transformation');

Gray level slicing
i=imread('C:\Users\vignan\Downloads\cutie.jpg');
j=double(i);
k=double(i);
[row,col]=size(j);
T1=input('Enter the Lowest threshold value:');
T2=input('Enter the Highest threshold value:');
for x=1:row
    for y=1:col
    if((j(x,y)>T1)&&(j(x,y)<T2))
        j(x,y)=i(x,y);
        k(x,y)=255;
    else
        j(x,y)=0;
        k(x,y)=0;
    end
    end
end
subplot(311),imshow(i),title('Originalimage')
subplot(312),imshow(uint8(j)),title('Graylevel slicing with background')
subplot(313),imshow(uint8(k)),title('Graylevel slicing without background')

Histogram equalization
I= imread("bts7.jpeg");
subplot(2,2,1), imshow(I),title("Original Image");
subplot(2,2,2),imhist(I);
J= histeq(I);
subplot(2,2,3),imshow(J), title("Histogram Equilised Image");
subplot(2,2,4),imhist(J);






Gaussian low pass filter
image=imread(' C:\Users\vignan\Documents\MATLAB\img1.jpeg');
gray_image=rgb2gray(image);
h_laplacian=fspecial('laplacian',0.2);
lpf_laplacian=imfilter(gray_image,h_laplacian);
h_gaussian=fspecial('gaussian',[5,5],1);
lpf_gaussian=imfilter(gray_image,h_gaussian);
figure;
subplot(1,3,1),imshow(gray_image),title('original image');
subplot(1,3,2),imshow(lpf_average),title('Low-pass filtered(Average)');
subplot(1,3,3),imshow(lpf_gaussian),title('Low-pass Filtered(Gaussian)');




Gaussian high pass filter
image = imread('C:\Users\vignan\Pictures\log.jpg');
h_laplacian = fspecial('laplacian', 0.2);
hpf_laplacian = imfilter(gray_image, h_laplacian);
h_gaussian = fspecial('gaussian', [5, 5], 1);
lpf_image = imfilter(gray_image, h_gaussian);
hpf_subtract = gray_image - lpf_image;
figure;
 subplot(1, 3, 1), imshow(gray_image), title('Original Image');
subplot(1, 3, 2), imshow(hpf_laplacian, []), title('High-Pass Filtered (Laplacian)');
subplot(1, 3, 3), imshow(hpf_subtract, []), title('High-Pass Filtered (Subtract)');




Gaussian lpf hpf using frequency domain
img=imread('C:\Users\vignan\Documents\MATLAB\shin.jpg');
img=double(rgb2gray(img));
F=fft2(img);
Fshift=fftshift(F);
[rows,cols]=size(img);
crow=round(rows/2);
ccol=round(cols/2);
radius=30;
[x,y]=meshgrid(1:cols,1:rows);
LPF=sqrt((x-ccol).^2+(y-crow).^2) <=radius;
F_LPF=Fshift.*LPF;
img_LPF=real(ifft2(ifftshift(F_LPF)));
HPF=double(sqrt((x-ccol).^2+(y-crow).^2)>radius);
F_HPF=Fshift.*HPF;
img_HPF=real(ifft2(ifftshift(F_HPF)));
figure;
subplot(1,3,1),imshow(img,[]),title('Original image');
subplot(1,3,2),imshow(img_LPF,[]),title('Low-Pass Filtered image');
subplot(1,3,3),imshow(img_HPF,[]),title('High-pass Filtered Image');






Edge detection
I = rgb2gray(imread('C:\Users\vignan\Documents\leena1.jpg')); 
subplot(2, 2, 1), 
imshow(I); 
title('Gray Scale Image'); 
% Sobel Edge Detection 
J = edge(I, 'Sobel'); 
subplot(2, 2, 2), 
imshow(J); 
title('Sobel'); 
% Prewitt Edge detection 
K = edge(I, 'Prewitt'); 
subplot(2, 2, 3), 
imshow(K); 
title('Prewitt'); 
% Robert Edge Detection 
L = edge(I, 'Roberts'); 
subplot(2, 2, 4), 
imshow(L); 
title('Robert');




 Sobel edge detection
k=imread('C:\Users\vignan\Documents\leena1.jpg');
k=rgb2gray(k);
k1=double(k);
p_msk=[-1 0 1; -2 0 2; -1 0 1];
kx=conv2(k1, p_msk, 'same');
ky=conv2(k1, p_msk', 'same');
subplot (2,2,1);
imshow(k);
subplot (2,2,2);
imshow(kx);
subplot (2,2,3);
imshow(ky);



Perwitt edge detection
k=imread('C:\Users\vignan\Documents\leena1.jpg');
k=rgb2gray(k);
k1=double(k);
p_msk=[-1 0 1; -1 0 1; -1 0 1];
kx=conv2(k1, p_msk, 'same');
ky=conv2(k1, p_msk', 'same');
subplot (2,2,1);
imshow(k);
subplot (2,2,2);
imshow(kx);
subplot (2,2,3);
imshow(ky);

Line detection
k=imread('C:\Users\vignan\Downloads\IMG-20240221-WA0004.jpg');  
k=im2bw(k);
w1=1/3*[1 0 -1;1 0 -1;1 0 -1];
g=(imfilter(double(k),w1));
g(g<1)=0;
subplot (2,2,1);
imshow(g);
title('Horizontal line');
w1=1/3*[1 1 1;0 0 0;-1 -1 -1];
g=(imfilter(double(k),w1));
g(g<1)=0;
subplot (2,2,2);
imshow(g);
title('Vertical line');
w1=1/3*[-1 -1 0;-1 0 1;0 1 1];
g=(imfilter(double(k),w1));
g(g<1)=0;
subplot (2,2,3);
imshow(g);
title('+45 Diagonal line');
w1=1/3*[0 -1 -1;1 0 -1;1 1 0];
g=(imfilter(double(k),w1));
g(g<1)=0;
subplot (2,2,4);
imshow(g);
title(‘-45 diagonal’);




Image segmentation
I=imread('C:\Users\vignan\Downloads\cam1.tif');
imshow(I);
title('Original Image');
mask=false(size(I));
mask(170,70)=true;
W=graydiffweight(I,mask,'GrayDifferenceCutoff',25);
thresh=0.01;
[BW,D]=imsegfmm(W,mask,thresh);
figure;
imshow(BW);
title('Segmented Image');

Morphological Thinning and Thickening
I=imread("bts8.jpeg");
subplot(2,2,1),imshow(I),title("Original Image");
binaryImg= im2bw(I);
subplot(2,2,2),imshow(binaryImg),title("Binary Image");
I_thin= bwmorph(binaryImg,'thin',5);
subplot(2,2,3),imshow(I_thin),title("Morphological Thinned Image");
I_thick= bwmorph(binaryImg, 'thick',5);
subplot(2,2,4),imshow(I_thick),title("Morphological Thicked Image");


Morphological operations
I=imread("bts8.jpeg");
subplot(2,3,1),imshow(I),title("Original Image");
SE= strel('line',7,7);
I_dil= imdilate(I,SE);
subplot(2,3,2),imshow(I_dil),title("Dilated Image");
I_erode= imerode(I,SE);
subplot(2,3,3),imshow(I_erode),title("Eroded Image");
I_open= imopen(I,SE);
subplot(2,3,4),imshow(I_open),title("Opened Image");
I_close= imclose(I,SE);
subplot(2,3,5),imshow(I_close),title("Closed Image");