Nothing Special   »   [go: up one dir, main page]
Scribd Logo
Upload

Welcome to Scribd!

  • 11 views

    Exam

    Uploaded by

    adisuadmasu42
    The document discusses various image processing techniques in MATLAB including reading images, converting between color spaces (RGB to grayscale, binary), resizing images, adding noise, and filtering noise using median, wiener and average filters. Example code is provided to load tiger.img, add salt and pepper noise, and filter it using median filtering. Further examples demonstrate converting images to grayscale and binary, resizing, and applying different filters like median, wiener, and average to remove noise.

    Copyright:

    © All Rights Reserved
    Download as RTF, PDF, TXT or read online from Scribd

    Exam

    Uploaded by

    adisuadmasu42
    11 views4 pages
    The document discusses various image processing techniques in MATLAB including reading images, converting between color spaces (RGB to grayscale, binary), resizing images, adding noise, and filtering noise using median, wiener and average filters. Example code is provided to load tiger.img, add salt and pepper noise, and filter it using median filtering. Further examples demonstrate converting images to grayscale and binary, resizing, and applying different filters like median, wiener, and average to remove noise.

    Original Description:

    Copyright

    © © All Rights Reserved

    Available Formats

    RTF, PDF, TXT or read online from Scribd

    Did you find this document useful?

    Is this content inappropriate?

    The document discusses various image processing techniques in MATLAB including reading images, converting between color spaces (RGB to grayscale, binary), resizing images, adding noise, and filtering noise using median, wiener and average filters. Example code is provided to load tiger.img, add salt and pepper noise, and filter it using median filtering. Further examples demonstrate converting images to grayscale and binary, resizing, and applying different filters like median, wiener, and average to remove noise.

    Copyright:

    © All Rights Reserved
    Download as RTF, PDF, TXT or read online from Scribd
    Download as rtf, pdf, or txt
    11 views4 pages

    Exam

    Uploaded by

    adisuadmasu42
    The document discusses various image processing techniques in MATLAB including reading images, converting between color spaces (RGB to grayscale, binary), resizing images, adding noise, and filtering noise using median, wiener and average filters. Example code is provided to load tiger.img, add salt and pepper noise, and filter it using median filtering. Further examples demonstrate converting images to grayscale and binary, resizing, and applying different filters like median, wiener, and average to remove noise.

    Copyright:

    © All Rights Reserved
    Download as RTF, PDF, TXT or read online from Scribd
    Download as rtf, pdf, or txt
    of 4

    LAB1

    RGB = imread('C:\Users\abu\Desktop\nova\tiger.img');
    figure, imshow(RGB)
    I = rgb2gray(RGB); % converting rgb image to grayscale image
    figure, imshow(I);
    im = im2bw(I); % converting rgb image to binary image
    figure, imshow(im);
    [X,map] = imread('tiger.img');
    imshow(X,map);
    i=imread('C:\Users\abu\Desktop\nova\tiger.img');
    subplot(221);
    figure,imshow(i);
    title('Working Matlab Editor');% how to give the matlab titles on the image
    j=rgb2gray(i); %converting from color image to grayscale
    subplot(222);
    imshow(j);
    k=im2bw(j); %converting grayscale image to binary image
    subplot(223);
    imshow(k);
    N = imresize(k, 0.5);
    figure(2),imshow(N);
    % adding a noise to the image and how to filter noize of the image
    Im1=imread('C:\Users\abu\Desktop\nova\tiger.img');
    imshow(Im1);
    J=imnoise(Im1,'salt & pepper',0.02);
    K=medfilt3(J);
    imshow(J), figure, imshow(K);

    RGB = imread('C:\Users\abu\Desktop\nova\tiger.img');
    figure, imshow(RGB)
    I = rgb2gray(RGB); % converting rgb image to grayscale image
    figure, imshow(I);
    im = im2bw(I); % converting rgb image to binary image
    figure, imshow(im);
    [X,map] = imread('tiger.img');
    imshow(X,map);
    i=imread('C:\Users\abu\Desktop\nova\tiger.img');
    subplot(221);
    figure,imshow(i);
    title('Working Matlab Editor');% how to give the matlab titles on the image
    j=rgb2gray(i); %converting from color image to grayscale
    subplot(222);
    imshow(j);
    k=im2bw(j); %converting grayscale image to binary image
    subplot(223);
    imshow(k);
    N = imresize(k, 0.5);
    figure(2),imshow(N);
    % adding a noise to the image and how to filter noize of the image
    Im1=imread('C:\Users\abu\Desktop\nova\tiger.img');
    imshow(Im1);
    J=imnoise(Im1,'salt & pepper',0.02);
    K=medfilt3(J);
    imshow(J), figure, imshow(K);
    filtering
    Imr=imread('Lena1.jpg');
    Im=rgb2gray(Imr);
    noizy=imnoise(Im,'salt & pepper',0.1);
    [m,n]=size(noizy);
    output=zeros(m,n);
    %output=unit8(output);
    for i=1:m
    for j=1:m
    xmin=max(1,i-1);
    xmax=min(m,i+1);
    ymin=max(1,j-1);
    ymax=min(m,j+1);
    temp=noizy(xmin:xmax,ymin:ymax);
    output(i,j)=median(temp(:));

    end
    end
    figure(1);
    set(gcf,'positon',get(0,'ScreenSize'));
    Subplot(131),imshow(Imr), title('Original Image');
    Subplot(132),imshow(Im), title('Noizy Image');
    Subplot(133),imshow(noizy), title('Median filtered Image');

    LAB2
    i=imread('A1.jpg');
    subplot(221);
    imshow(i);
    title('Original image');
    title('Working Matlab Editor');% how to give the matlab titles on the image

    %matlab

    %lab sessions
    %resizing images
    %converting rgb image to binary
    %and gray scale
    % Ploting figures
    %properties of image
    %figure vs subplot
    Img=imread('C:\Users\Dell\Desktop\matl\lena1.jpg');
    figure,imshow(Img);
    title('Orignal Image');
    whos Img;
    Imgg=imresize(Img,2);
    figure,imshow(Imgg);
    title('Resized Image');
    whos Imgg;
    Img1=rgb2gray(Img);
    figure,imshow(Img1);
    title('Grayscale Image');
    whos Img1;
    k=im2bw(Img1);
    figure, imshow(k);
    title('Binary image');
    whos k;

    ////////////////////
    Img=imread('C:\Users\Dell\Desktop\matl\lena1.jpg');
    subplot(221),imshow(Img);
    title('Orignal Image');
    whos Img;
    Imgg=imresize(Img,2);
    subplot(222),imshow(Imgg);
    title('Resized Image');
    whos Imgg;
    Img1=rgb2gray(Img);
    subplot(223),imshow(Img1);
    title('Grayscale Image');
    whos Img1;
    k=im2bw(Img1);
    subplot(224), imshow(k);
    title('Binary image');
    whos k;

    %converting grayscale image to binary image


    subplot(223);
    imshow(k);
    title('Binary image');
    N = imresize(k, 2);
    figure(2),imshow(N);
    title('Resized Image');

    MID FILTERING

    Img1=imread('LL.png');
    [h,w]=size(Img1);
    figure(1), imshow(Img1);
    title ('Orignal image');
    %resize
    N = imresize(Img1, 0.5);
    figure(2),imshow(N);
    title(' Rezised Image');
    % Adding Noise to The Image
    J = imnoise(Img1,'salt & pepper',0.01);
    figure(3), imshow(J);
    title(' image with salt and Noise 0.01 ');
    J = imnoise(N,'salt & pepper',0.2);
    figure(4),imshow(J);
    title ('Image with noise level salt and 0.4');
    %Before binarization
    b1=rgb2gray(J);
    a=medfilt2(b1,[3,3]);
    figure(5), imshow(a);
    title ('Median Filtered Image');
    K = wiener2(a,[1.5 1.5]);
    figure(6), imshow(K);
    title('wiener2 filtered image');
    % Average filtering
    P = filter2(fspecial('average',2),Img1)/255;
    figure(7), imshow(P);
    title ('Average filtering');
    %After binarization
    BW=dither(Img1);
    figure(8),imshow(BW);
    title ('Binary Image');
    BW1 = im2bw(Img1);
    figure(9),imshow(BW1);
    title ('Gray scale image');
    %%im2bw(I,level);% converts grayscale image to a binary Img1;
    %level is in the range [0,1]
    BW2 = im2bw(Img1, 0.6);
    figure(10),imshow(BW2);
    title ('RGB to a binary Img1');
    %converts the truecolor Img1 RGB to a binary Img1
    %Gaussian filter using MATLAB built_in function
    %Read an Image
    A = imnoise(Img1,'Gaussian',0.02,0.03);
    %Image with noise
    figure(11),imshow(A);
    title('Gaussian Imgae Noise');

    You might also like