Matlab for Image Processing Course

Learn Image Processing with Matlab

Created byOnesinus Tamba

Last updated 3/2026

English

What you'll learn

Fundamental Image Processing Techniques
Build Confidence in Implementing Image Processing Algorithms
Develop Practical Skills
Analytical Thinking Through Image Analysis

Course content

3 sections • 25 lectures • 2h 54m total length

Introduction1:04
Matlab Online7:51
Basic Commands / Functions - #16:40
img = imread('ct-scan-abdominal.jpg');
imshow(img);
img2 = imread('diabetic-retinopaty.jpg');
imshow(img2);
img2Gray = rgb2gray(img2);
imshow(img2Gray);
Basic commands and functions - #220:16
% Load an image
img = imread('image.jpg');

% Display the image
imshow(img);

% Convert the image to grayscale
grayImg = rgb2gray(img);

% Display the grayscale image
imshow(grayImg);

% Resize the image to half its original size
resizedImg = imresize(img, 0.5);

% Display the resized image
imshow(resizedImg);

% Crop a portion of the image
croppedImg = imcrop(img, [50 50 200 200]); % [x, y, width, height]

% Display the cropped image
imshow(croppedImg);

% Show the histogram of a grayscale image
imhist(grayImg);

img1=img1(:,:,1);

% Convert grayscale image to binary using a threshold
binaryImg = imbinarize(grayImg, 0.5); % Threshold of 0.5

% Display the binary image
imshow(binaryImg);

% Apply Canny edge detection
edges = edge(img1, 'Canny');

% Display the edges
imshow(edges);

% Apply Gaussian filter for smoothing
smoothedImg = imgaussfilt(img, 2); % Sigma = 2

% Display the smoothed image
imshow(smoothedImg);

% Rotate the image by 45 degrees
rotatedImg = imrotate(img, 45);

% Display the rotated image
imshow(rotatedImg);

% Save the processed image
imwrite(grayImg, 'gray_image.jpg');
Image processing methods - Image Conversion7:56
image1 = imread('diabetic-retinopaty.jpg');
figure(1);
imshow(image1);
figure(2);
image2 = rgb2gray(image1);
imshow(image2);
figure(3);
image3 = rgb2hsv(image1);
imshow(image3);
Image processing methods - Morphological Operations11:06
SE = strel('disk', 5);
image2 = imdilate(image1, SE);
figure(2);
imshow(image2);
Image processing methods - Image Segmentation14:06
image0 = imread('grayscale.jpg');
figure(1);
imshow(image0);

threshold = graythresh(image0);
image2 = imbinarize(image0, threshold);
figure(2);
imshowpair(image0,image2,'montage');

% Using K-means
[L,Centers] = imsegkmeans(image0,3);
B = labeloverlay(image0,L);
imshow(B)
title("Labeled Image")
Image processing methods - Histogram Equalization4:53
image3 = histeq(image0);
figure(2);
imshow(image3);
figure(3);
imhist(image0, 64);
figure(4);
imhist(image3, 64);
Image processing methods - Add and Remove Noise from an image7:33
image2 = imnoise(image0, 'gaussian', 0.01);
figure(2);
imshow(image2);
image3 = imnoise(image0, 'salt & pepper', 0.09);
figure(3);
imshow(image3);
image4 = medfilt2(rgb2gray(image3));
figure(4);
imshow(image4);
Image processing methods - Image collage2:54
collage1 = [image0, image0; image0 image0];
figure(2);
imshow(collage1);
Image processing methods - Basic Image Arithmetic7:04
image2 = image0 + 100;
figure(2);
imshow(image2);
image3 = image0 - 100;
figure(3);
imshow(image3);
image4 = image0 * 2;
figure(4);
imshow(image4);
image5 = image0 / 2;
figure(5);
imshow(image5);
Image processing methods - Negative Image (max intensity)3:01
image2 = 255 - image0;
figure(2);
imshow(image2);
image3 = 150 + image0;
figure(3);
imshow(image3);
Image processing methods - Flip image horizontally and vertically3:57
image2 = flip(image0, 2);
image3 = flip(image0, 1);
figure(2);
subplot(1, 3, 1), imshow(image0), title('Original Image');
subplot(1, 3, 2), imshow(image2), title('Image Flipper horizontally');
subplot(1, 3, 3), imshow(image3), title('Image Flipper vertically');
Image processing methods - Channel Separation (Red, Green, Blue Channels)4:56
imshow(image1);
r_image1 = image1(:,:,1);
g_image1 = image1(:,:,2);
b_image1 = image1(:,:,3);
figure(2);
subplot(1, 3, 1), imshow(r_image1), title('Red Channel');
subplot(1, 3, 2), imshow(g_image1), title('Green Channel');
subplot(1, 3, 3), imshow(b_image1), title('Blue Channel');
Image processing methods - Image Filtering using conv28:08
image2 = rgb2gray(image1);
imshow(image2);
kernel = ones(3, 3) / 9;
image3 = conv2(double(image2), kernel, 'same');
figure(2);
imshow(uint8(image3));

Introduction - Learning by use cases1:51
Use Case 1 - Automatic Contrast Adjustment4:01
img = imread('ct-scan-abdominal.jpg');
img_gray = rgb2gray(img);
img_enh = histeq(img_gray);
figure;
subplot(1, 2, 1);
imshow(img_gray);
title('Original Image');
subplot(1, 2, 2);
imshow(img_enh);
title('Enhanced Image');
Use Case 2 - Object Counting in a Binary Image7:31
grayImg = imread('rice.png');
figure;
imshow(grayImg);
binaryImg = imbinarize(grayImg);
[labeledImg, numObjects] = bwlabel(binaryImg);
figure;
subplot(1, 2, 1);
imshow(binaryImg);
title('Binary Image');
subplot(1, 2, 2);
imshow(label2rgb(labeledImg));
title(['Objects Counted: ', num2str(numObjects)]);
Use Case 3 - Edge Detection in an Image7:11
img = imread('grayscale.jpg');
imgGray = rgb2gray(img);
img1 = edge(imgGray, 'canny');
figure;
subplot(1, 2, 1);
imshow(img);
title('Original Image');
subplot(1, 2, 2);
imshow(img1);
title('Edge Detection Image');
Use Case 4 - Image Sharpening7:36
img = imread('grayscale.jpg');
img1 = rgb2gray(img);
lapcianFilter = [0, -1, 0; -1 5 -1; 0 -1 0];
img2 = imfilter(img1, lapcianFilter);
figure;
subplot(1, 2, 1);
imshow(img1);
title('Gray Image');
subplot(1, 2, 2);
imshow(img2);
title('Sharpened Image');
Use Case 5 - Image Smoothing (Blurring) Using a Gaussian Filter3:51
img = imread('ct-scan-abdominal.jpg');
img1 = rgb2gray(img);
gaussianFilter = fspecial("gaussian", [5,5], 2);
img2 = imfilter(img1, gaussianFilter, 'symmetric');
figure;
subplot(1,2,1);
imshow(img1);
title('Original Image');
subplot(1,2,2);
imshow(img2);
title('Blurred Image');
Create own function 1 - Simple threshold to create a binary image8:26
customProcessImage.m
function binaryImage = customProcessImage(image_path, threshold)
img = imread(image_path);
if size(img, 3) == 3
grayImg = rgb2gray(img);
else
grayImg = img;
end
binaryImage = grayImg > threshold;
figure;
subplot(1, 2, 1);
imshow(grayImg);
title('Grayscale Image');
subplot(1, 2, 2);
imshow(binaryImage);
title('Binary Image');
imwrite(binaryImage, 'binary_image_result.png');
end

To call the function
customProcessImage('grayscale.jpg', 128);
Create own function 2 - Gaussian Blur4:40
applyGaussianBlur.m
function blurredImage = applyGaussianBlur(image_path, sigma)
img = imread(image_path);
if size(img, 3) == 3
grayImg = rgb2gray(img);
else
grayImg = img;
end
blurredImage = imgaussfilt(grayImg, sigma);
figure;
subplot(1, 2, 1);
imshow(grayImg);
title('Grayscale Image');
subplot(1, 2, 2);
imshow(blurredImage);
title('Blurred Image');
end

Call the function
applyGaussianBlur('grayscale.jpg', 2);
Create own function 3 - Edge Detection10:25
edgeDetection.m
function edgeImage = edgeDetection(image_path)
img = imread(image_path);
if size(img, 3) == 3
grayImg = rgb2gray(img);
else
grayImg = img;
end
% Logics
sobelX = [-1 0 -1; -2 0 2; -1 0 1];
sobelY = [-1 2 -1; 0 0 0; 1 2 1];
[rows, cols] = size(grayImg);
edgeImage = zeros(rows, cols);
for i = 2: rows - 1
for j = 2: cols - 1
neighborhood = double(grayImg(i-1:i+1, j-1:j+1));
Gx = sum(sum(neighborhood .* sobelX));
Gy = sum(sum(neighborhood .* sobelY));
edgeImage(i, j) = sqrt(Gx^2 + Gy^2);
end
end
edgeImage = uint8(255 * mat2gray(edgeImage));
% Display
figure;
subplot(1, 2, 1);
imshow(grayImg);
title('Grayscale Image');
subplot(1, 2, 2);
imshow(edgeImage);
title('Edge Detected Image');
end

Using the function
edgeDetection('grayscale.jpg');
edgeDetection('diabetic-retinopaty.jpg');
edgeDetection('ct-scan-abdominal.jpg');

Requirements

Everyone can learn as long as they have laptop, internet and willingness to learn
laptop
internet
willingness to learn

Description

This course is designed for beginner to introduce participants to the fundamental concepts and practical applications of image processing using MATLAB. No prior experience in image processing is required, making it the perfect starting point for anyone new to this exciting field. Throughout the course, participants will explore the essential techniques used to manipulate, analyze, and enhance digital images.

Starting with the basics, learners will be guided through core operations such as loading, displaying, and saving images. They will practice manipulating images through simple operations like resizing, rotating, flipping, and adjusting brightness or contrast. By the end of the foundational lessons, participants will have a solid understanding of how to apply basic filters and transformations to images.

As the course progresses, participants will engage in hands-on study cases that bring real-world relevance to their learning. For example, they will learn how to enhance the contrast of poorly lit images, count objects in binary images, and perform basic segmentation techniques. These practical exercises will not only solidify their understanding of core concepts but also give them the confidence to solve real-world problems using MATLAB.

The course emphasizes hands-on learning, with numerous coding exercises that reinforce each concept. By the end of the course, participants will have the skills needed to approach more advanced image processing tasks, setting the stage for future exploration in this growing field.

Who this course is for:

Everyone can learn
Student
Programmer
Tech People

Matlab for Image Processing Course

What you'll learn

Explore related topics

Course content

Basic Image Processing using Matlab15 lectures • 1hr 51min

Learning by use cases9 lectures • 56min

[BONUS]: Image Processing using Python1 lecture • 8min

Requirements

Description

Who this course is for: