Adaptive Median Filter Function for Digital Image Processing
MATLAB implementation (.m file) of adaptive median filter for digital image processing with noise reduction capabilities
Digital Image Processing: Object Perimeter Measurement, Center Detection, and Minimum Bounding Rectangle Implementation
This program focuses on measuring object perimeter and detecting center points in digital image processing, with additional functionality for drawing minimum bounding rectangles around detected objects. It implements key computer vision algorithms including edge detection, thresholding, morphological operations, and image segmentation techniques.
Digital Image Processing MATLAB Toolbox
A comprehensive MATLAB toolbox for digital image processing, featuring essential source codes for color image segmentation, RGB component extraction, and other fundamental operations with implementation details and algorithm explanations.
Smoothing and Sharpening (Edge Detection) in Digital Image Processing
Smoothing and Sharpening (Edge Detection) in Digital Image Processing. Includes: 1. Adding salt-and-pepper and Gaussian noise. 2. Smoothing noise-contaminated images using neighborhood averaging, median filtering, and K-nearest neighbor averaging methods. 3. Sharpening images using Roberts gradient, Sobel operator, and Laplacian operator methods with comparative result analysis. Accompanied by source image and processed result screenshots with implementation code insights.
18 Classic Grayscale Images for Digital Image Processing
18 classic grayscale images for digital image processing, serving as optimal benchmarks for implementing various image processing algorithms. These images have gained universal recognition among scholars worldwide for their representative characteristics. Furthermore, these 18 images constitute the standard test suite utilized in our laboratory for algorithm validation and system performance evaluation.
Undergraduate Digital Image Processing Experiment Program 2
Undergraduate Digital Image Processing Experiment Program 2, covering image sharpening, segmentation, and related techniques with code implementation examples
MATLAB Implementation of K-Nearest Neighbors (KNN) Classifier
The K-Nearest Neighbors (KNN) classifier is a widely used classification method in digital image processing and analysis, implemented through distance metrics and majority voting algorithms.
MATLAB Source Code for Iterative Blind Deconvolution Algorithm in Digital Image Processing for Image Deblurring
MATLAB implementation of iterative blind deconvolution algorithm for image deblurring in digital image processing, featuring complete source code for restoring blurred images to sharp, recognizable quality
MATLAB Program for Low-Pass Filter Generation in Digital Image Processing
This MATLAB program generates low-pass filters specifically designed for digital image processing applications, featuring customizable parameters and efficient algorithm implementation.
Grayscale Histogram as the Simplest and Most Useful Tool
In digital image processing, the grayscale histogram stands out as the simplest and most valuable tool. It plays a fundamental role throughout the entire workflow—from analyzing and observing images to developing effective processing methods. In programming terms, histograms can be efficiently computed using functions like `cv2.calcHist()` in OpenCV or `imhist()` in MATLAB, which count pixel frequencies across intensity levels to reveal critical image characteristics.