Complete Guide to Image Processing with MATLAB

Learn to read an image in MATLAB with imread, store it in a variable, determine its size (rows, columns, channels), display it, and inspect pixel RGB values using image tool.

Convert a color image to grayscale in MATLAB using rgb2gray, showing the resulting one-channel image with 0-255 intensity, and explore LAB luminance and channel extraction for alternative representations.

Learn to compute the mean and the sum of an image in MATLAB, using vector and image matrix examples, including per-column means for a grayscale image.

Read an image in MATLAB, convert it to grayscale, save it to a directory with a new name and format, then read and display the grayscale image.

Process dozens of images in MATLAB with a four-line workflow that reads from a folder, uses a for loop, converts each image to grayscale, and saves results to a directory.

Learn to plot a grayscale image histogram in MATLAB, showing the intensity distribution from 0 to 255 and the number of pixels at each intensity.

Learn to plot color image histograms in MATLAB by splitting an image into red, green, and blue channels. Also extract patches and demonstrate rotation.

Explore histogram equalization to boost image contrast in MATLAB by redistributing pixel intensities, applying it to grayscale and color images via the luminance channel in a color space.

Explore adaptive histogram equalization in MATLAB, which enhances local contrast by applying histogram distribution to small image tiles and fusing them for a global result.

Learn how to create an average filter in MATLAB by hand and with fspecial, using 3x3, 5x5, and 9x9, apply via convolution, and compare blur levels.

Apply median filtering in MATLAB to remove salt and pepper noise from grayscale and color images, using default 3x3 masks or larger sizes, with discussion of 3D filtering.

Explore how to create and apply a motion blur filter in MATLAB, and then recover the motion-blurred image with Wiener deconvolution, highlighting convolution and circular filter shapes.

this lecture demonstrates applying the Gaussian filter in MATLAB to smooth images, showing how sigma controls blur, with default 0.5 and stronger smoothing as sigma grows.

Explore how MATLAB thresholding converts images to binary by applying a threshold, turning crossing pixels white and others black after converting to grayscale and scaling to 0-1 values.

Explore edge detection in MATLAB using the edge function with sobel, canny, prewitt, and laplacian of gaussian. See how thresholds and noise affect edge visibility.

Explore image sharpening in MATLAB by loading a grayscale image, applying sharpening with an adjustable amount, and comparing original and sharpened results. See how increasing the sharpening factor enhances details.

Explore image morphology in MATLAB by applying erosion with various structuring elements, showing how square and diamond shapes shrink images, remove noise, and reveal size effects.

Apply dilation in MATLAB to expand images using a square structuring element, observe how increasing sizes affect connectivity, and compare the results with erosion.

Open in MATLAB, opening erodes then dilates to remove noise while preserving original information, using a structuring element like a square and evaluating with SSIM.

Learn how closing in MATLAB uses dilation and erosion with structuring elements to expand and shrink an image, filling holes and preserving shape.

Explore local binary patterns (LBP), a simple, efficient method for extracting texture features from images. Use these robust, real-time features for classification, detection, and recognition, even under illumination changes.

Explore local binary patterns in MATLAB by reading floor and brick images, generating a rotated version, extracting LBP features, and comparing them using a mean square error to assess similarity.

Apply local binary patterns to brick images in Matlab, convert them to grayscale, extract lbp features, and compare texture similarities via histograms, with potential use in image classification.

Explore template matching by locating a small image region in a larger image using cross-correlation. Contrast with convolution by noting the 180-degree rotation and padding.

Learn how to perform template matching in MATLAB by cropping a template from an image and locating it in the full image using normalized cross-correlation, coordinates, and plotted figures.

Explore more MATLAB examples across image processing and computer vision toolboxes, including image segmentation with k-means, object analysis, feature extraction, matching, and deep learning applications.

Watch a complete gui demo that loads images, converts to grayscale, processes color images, applies histogram equalization, noise addition, filtering, object detection, morphology, and PSNR and structure similarity assessments.

Learn to design a MATLAB GUI with GUIDE, add buttons, radios, checkboxes, and text fields, adjust fonts and colors, save the UI, and wire functions for image display in axes.

Learn to build a MATLAB GUI for image processing: load images, use callbacks, define global variables, and display grayscale or black-and-white images with interactive controls.

Explore MATLAB GUI callbacks by using global image variables to convert an rgb image to grayscale, auto-threshold to black-and-white, display on axes, and show the histogram for contrast enhancement.

Create a MATLAB GUI to show original and equalized histograms, extract text from a grayscale image with the built-in OCR, and display results in a static text field.

Explore image morphology in a MATLAB GUI by selecting a square or diamond structuring element and a size (3x3, 4x4, 5x5), then apply erosion, opening, and closing via callback-driven controls.

Load an image, convert to black and white, and label and count objects using black-and-white labeling, image complement, and four- or eight-connectivity; display centroids with regionprops.

Explore structure similarity (SSIM) to compare two images, including loading, corrupting with noise, and measuring similarity, while resizing the second image with cubic interpolation when sizes differ.

Learn to build a GUI for edge detection using canny, prewitt, and sobel, and magnify images with resize options like bilinear, bicubic, and nearest neighbor, including saving the magnified output.

Explore adding Gaussian noise to grayscale and RGB images, apply filters (average, median, guided), compare PSNR and SNR, and rotate images in a MATLAB GUI.