Extracting Five Texture Features Using Gray Level Co-occurrence Matrix
Extract five key texture features using Gray Level Co-occurrence Matrix (GLCM): entropy, homogeneity, correlation, energy, and contrast
Histogram Equalization
Histogram equalization is an image processing technique that redistributes pixel intensities to transform an image's histogram from a concentrated range to a uniform distribution across the entire grayscale spectrum. This non-linear stretching operation enhances contrast by making pixel counts approximately equal within intensity ranges. While effective for improving local contrast, it may reduce grayscale levels and cause unnatural over-enhancement in high-peak histograms. Implementation typically involves calculating cumulative distribution functions and mapping original intensities to new values.
MATLAB Implementation of Retinex Algorithm for Image Enhancement
A Retinex algorithm source code implementation for image contrast enhancement and quality improvement, featuring multi-scale processing and Gaussian filtering techniques
MATLAB Implementation of Homomorphic Filtering
Homomorphic filtering algorithm implementation in MATLAB - effectively compresses dynamic range and enhances image contrast through frequency domain processing.
The Six Components of Tamura Texture Features
The six components of Tamura texture features correspond to six perceptual texture attributes from a psychological perspective: coarseness, contrast, directionality, linelikeness, regularity, and roughness. The first three components are particularly significant for image retrieval applications. This MATLAB toolkit includes both main functions and sub-functions for computing Tamura texture features, implementing the complete algorithm pipeline from image preprocessing to feature vector extraction.
Image Enhancement: Techniques to Improve Picture Quality
The objective of image enhancement is to improve picture quality by increasing contrast, reducing blur and noise, and correcting geometric distortions, while image restoration is a technique that estimates the original image assuming known models of blur or noise. Image enhancement methods are categorized into frequency domain and spatial domain approaches. Frequency domain methods treat images as 2D signals and employ 2D Fourier transform for signal enhancement, with low-pass filtering to remove noise and high-pass filtering to sharpen edges. Spatial domain algorithms include local averaging and median filtering (using the median pixel value in a local neighborhood) for noise reduction.
Calculation of Image Contrast in MATLAB
MATLAB Image Contrast Calculation - This file contains multiple images with varying contrast levels and demonstrates how to compute contrast using different algorithms
MATLAB Image Processing Algorithms: Sharpening, Contrast Adjustment, and Noise Removal
This document presents three essential image processing algorithms implemented in MATLAB: Laplacian-based image sharpening, contrast adjustment techniques, and median filter noise reduction. Ideal for MATLAB beginners and image processing enthusiasts, with code implementation insights and practical applications.
Keypoint Detection for SIFT Transform
Keypoint detection for SIFT transformation involving contrast determination and Hessian matrix calculation, with final output providing coordinates of keypoints within the Difference of Gaussian (DOG) space!
Image Contrast Enhancement Using Gray-Level Stretching
Implementation of image contrast enhancement using MATLAB, employing the gray-level stretching method with code examples and algorithm explanation