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Top 30 MATLAB Image Processing Functions

#MATLAB #ImageProcessing #Basics

#1. imread()
Reads an image from a file into a matrix.

img = imread('peppers.png');
disp('Image "peppers.png" loaded into variable "img".');

Image "peppers.png" loaded into variable "img".

#2. imshow()
Displays an image in a figure window.

img = imread('peppers.png');
imshow(img);
title('Peppers Image');

Output: A new figure window opens, displaying the 'peppers.png' image with the title "Peppers Image".

#3. imwrite()
Writes an image matrix to a file.

img = imread('cameraman.tif');
imwrite(img, 'my_cameraman.jpg');
disp('Image saved as my_cameraman.jpg');

Image saved as my_cameraman.jpg

#4. size()
Returns the dimensions of the image matrix (rows, columns, color channels).

rgb_img = imread('peppers.png');
gray_img = imread('cameraman.tif');
size_rgb = size(rgb_img);
size_gray = size(gray_img);
disp(['Size of RGB image: ', num2str(size_rgb)]);
disp(['Size of grayscale image: ', num2str(size_gray)]);

Size of RGB image: 384   512     3
Size of grayscale image: 256   256

#5. rgb2gray()
Converts an RGB color image to a grayscale intensity image.

rgb_img = imread('peppers.png');
gray_img = rgb2gray(rgb_img);
imshow(gray_img);
title('Grayscale Peppers');

Output: A figure window displays the grayscale version of the peppers image.

---
#MATLAB #ImageProcessing #Conversion #Transformation

#6. im2double()
Converts an image to double-precision format, scaling data to the range [0, 1].

img_uint8 = imread('cameraman.tif');
img_double = im2double(img_uint8);
disp(['Max value of original image: ', num2str(max(img_uint8(:)))]);
disp(['Max value of double image: ', num2str(max(img_double(:)))]);

Max value of original image: 253
Max value of double image: 0.99216

#7. imresize()
Resizes an image to a specified size.

img = imread('cameraman.tif');
resized_img = imresize(img, 0.5); % Resize to 50% of original size
imshow(resized_img);
title('Resized Cameraman');

Output: A figure window displays the cameraman image at half its original size.

#8. imrotate()
Rotates an image by a specified angle.

img = imread('cameraman.tif');
rotated_img = imrotate(img, 30, 'bilinear', 'crop');
imshow(rotated_img);
title('Rotated 30 Degrees');

Output: A figure window displays the cameraman image rotated by 30 degrees, cropped to the original size.

#9. imcrop()
Crops an image to a specified rectangle.

img = imread('peppers.png');
% [xmin ymin width height]
cropped_img = imcrop(img, [100 80 250 200]);
imshow(cropped_img);
title('Cropped Image');

Output: A figure window displays only the rectangular section specified from the peppers image.

#10. rgb2hsv()
Converts an RGB image to the Hue-Saturation-Value (HSV) color space.

rgb_img = imread('peppers.png');
hsv_img = rgb2hsv(rgb_img);
hue_channel = hsv_img(:,:,1); % Extract the Hue channel
imshow(hue_channel);
title('Hue Channel of Peppers Image');

Output: A figure window displays the Hue channel of the peppers image as a grayscale image.

---
#MATLAB #ImageProcessing #Enhancement

#11. imhist()
Displays the histogram of an image, showing the distribution of pixel intensity values.

gray_img = imread('pout.tif');
imhist(gray_img);
title('Histogram of a Low-Contrast Image');

Output: A figure window with a bar chart showing the intensity distribution of the 'pout.tif' image.

#12. histeq()
Enhances contrast using histogram equalization.

low_contrast_img = imread('pout.tif');
high_contrast_img = histeq(low_contrast_img);
imshow(high_contrast_img);
title('Histogram Equalized Image');

Output: A figure window displays a higher contrast version of the 'pout.tif' image.

#13. imadjust()
Adjusts image intensity values or colormap by mapping intensity values to new values.

img = imread('cameraman.tif');
adjusted_img = imadjust(img, [0.3 0.7], []);
imshow(adjusted_img);
title('Intensity Adjusted Image');

Output: A figure window showing a high-contrast version of the cameraman image, where intensities between 0.3 and 0.7 are stretched to the full [0, 1] range.

#14. imtranslate()
Translates (shifts) an image horizontally and vertically.

img = imread('cameraman.tif');
translated_img = imtranslate(img, [25, 15]); % Shift 25 pixels right, 15 pixels down
imshow(translated_img);
title('Translated Image');

Output: A figure window shows the cameraman image shifted to the right and down.

#15. imsharpen()
Sharpens an image using the unsharp masking method.

img = imread('peppers.png');
sharpened_img = imsharpen(img);
imshow(sharpened_img);
title('Sharpened Image');

Output: A figure window displays a crisper, more detailed version of the peppers image.

---
#MATLAB #ImageProcessing #Filtering #Noise

#16. imnoise()
Adds a specified type of noise to an image.

img = imread('cameraman.tif');
noisy_img = imnoise(img, 'salt & pepper', 0.02);
imshow(noisy_img);
title('Image with Salt & Pepper Noise');

Output: A figure window displays the cameraman image with random white and black pixels (noise).

#17. fspecial()
Creates a predefined 2-D filter kernel (e.g., for averaging, Gaussian blur, Laplacian).

h = fspecial('motion', 20, 45); % Create a motion blur filter
disp('Generated a 2D motion filter kernel.');
disp(h);

Generated a 2D motion filter kernel.
(Output is a matrix representing the filter kernel)

#18. imfilter()
Filters a multidimensional image with a specified filter kernel.

img = imread('cameraman.tif');
h = fspecial('motion', 20, 45);
motion_blur_img = imfilter(img, h, 'replicate');
imshow(motion_blur_img);
title('Motion Blurred Image');

Output: A figure window shows the cameraman image with a motion blur effect applied at a 45-degree angle.

#19. medfilt2()
Performs 2-D median filtering, which is excellent for removing 'salt & pepper' noise.

noisy_img = imnoise(imread('cameraman.tif'), 'salt & pepper', 0.02);
denoised_img = medfilt2(noisy_img);
imshow(denoised_img);
title('Denoised with Median Filter');

Output: A figure window shows the noisy image significantly cleaned up, with most salt & pepper noise removed.

#20. edge()
Finds edges in an intensity image using various algorithms (e.g., Sobel, Canny).

img = imread('cameraman.tif');
edges = edge(img, 'Canny');
imshow(edges);
title('Edges found with Canny Detector');

Output: A figure window displays a binary image showing only the detected edges from the original image in white.

---
#MATLAB #ImageProcessing #Segmentation #Morphology

#21. graythresh()
Computes a global image threshold from a grayscale image using Otsu's method.

img = imread('coins.png');
level = graythresh(img);
disp(['Optimal threshold level (Otsu): ', num2str(level)]);

Optimal threshold level (Otsu): 0.49412

#22. imbinarize()
Converts a grayscale image to a binary image based on a threshold.

img = imread('coins.png');
level = graythresh(img); % Find optimal threshold
bw_img = imbinarize(img, level);
imshow(bw_img);
title('Binarized Image (Otsu Method)');

Output: A figure window displays a black and white image of the coins.

#23. strel()
Creates a morphological structuring element (SE), which is used to probe an image in morphological operations.

se = strel('disk', 5);
disp('Created a disk-shaped structuring element with radius 5.');
disp(se);

Created a disk-shaped structuring element with radius 5.
(Output describes the strel object and shows its matrix representation)

#24. imdilate()
Dilates a binary image, making objects larger and filling small holes.

img = imread('text.png');
se = strel('line', 3, 90); % A vertical line SE
dilated_img = imdilate(img, se);
imshow(dilated_img);
title('Dilated Text');

Output: A figure window shows the text characters appearing thicker, especially in the vertical direction.

#25. imerode()
Erodes a binary image, shrinking objects and removing small noise.

img = imread('text.png');
se = strel('line', 3, 0); % A horizontal line SE
eroded_img = imerode(img, se);
imshow(eroded_img);
title('Eroded Text');

Output: A figure window shows the text characters appearing thinner, with horizontal parts possibly disappearing.

---
#MATLAB #ImageProcessing #Analysis

#26. imopen()
Performs morphological opening (erosion followed by dilation). It smooths contours and removes small objects.

original = imread('circbw.tif');
se = strel('disk', 10);
opened_img = imopen(original, se);
imshow(opened_img);
title('Morphologically Opened Image');

Output: A figure window displays the image with small protrusions removed and gaps between objects widened.

#27. bwareaopen()
Removes all connected components (objects) from a binary image that have fewer than a specified number of pixels.

img = imread('text.png');
cleaned_img = bwareaopen(img, 50); % Remove objects with fewer than 50 pixels
imshow(cleaned_img);
title('Image after removing small objects');

Output: A figure window shows the text image with small noise specks or broken parts of characters removed.

#28. bwlabel()
Labels connected components in a binary image.

img = imread('text.png');
[L, num] = bwlabel(img);
disp(['Number of connected objects found: ', num2str(num)]);

Number of connected objects found: 114

#29. regionprops()
Measures a set of properties for each labeled region in an image.