Histogram Equalization and Image Sharpening
Histogram Equalization and Image Sharpening - Image Enhancement Techniques with Implementation Approaches
Histogram Equalization
Implementation of histogram equalization code in MATLAB with concise and elegant algorithmic approach
Image Enhancement Algorithms: Grayscale Transformation, Histogram Equalization, and Pseudocolor Enhancement
MATLAB implementation of image enhancement algorithms utilizing grayscale transformation, histogram equalization, and pseudocolor enhancement techniques with satisfactory performance results
RSIHE Algorithm (Recursive Sub-Image Histogram Equalization) with MATLAB Implementation and Technical Documentation
RSIHE Algorithm (Recursive Sub-Image Histogram Equalization) - An advanced image enhancement technique that recursively divides images into 2^r equal-area sub-images for localized histogram equalization. This package includes MATLAB source code, research paper, and sample input images with detailed implementation of the recursive partitioning and adaptive enhancement process.
Comprehensive Graphic and Image Processing Techniques
MATLAB-based multi-faceted image processing workflow: starting with histogram equalization for contrast enhancement, followed by integral projection analysis (including horizontal and vertical projections), and concluding with precise target localization algorithms.
RMSHE Algorithm (Recursive Mean-Separate Histogram Equalization)
RMSHE algorithm (Recursive Mean-Separate Histogram Equalization). Core principle involves segmenting images based on mean grayscale values and performing histogram equalization on each segment separately. The package includes MATLAB source code implementation, research paper documentation, and input test images for comprehensive evaluation.
MATLAB Implementation of Color Image Processing
Color image processing techniques including RGB channel decomposition, grayscale conversion, histogram equalization, and false color image synthesis with MATLAB code implementation details
License Plate Recognition, Noise Removal via Image Stacking, Filter-Based Denoising, and Color Image Processing
MATLAB-based license plate recognition, noise removal techniques using image stacking and filtering, color image processing, and histogram equalization methods with implementation insights
Complete Fingerprint Recognition System
This comprehensive fingerprint recognition program implements key processing stages including histogram equalization, Gabor filter-based image enhancement, orientation field filtering, ridge thinning, feature extraction, and feature matching. The system incorporates three distinct matching algorithms and includes supplementary presentation materials detailing implementation approaches, making it highly valuable for research and study.