MATLAB Code Implementation for Image Fusion
Implementing image fusion using MATLAB with support for image processing and visualization
Three Algorithms for IHS and PCA Weighted Image Fusion
MATLAB source code implementations for three image fusion algorithms: IHS, PCA, and weighted fusion method
Contourlet Region Statistical Method for Image Fusion
Contourlet region statistical method for performing image fusion with implementation insights on multi-scale decomposition and directional filter banks
MATLAB Programs for Hyperspectral Image Processing
Comprehensive MATLAB programs for hyperspectral image processing featuring image fusion, dimensionality reduction, and maximum likelihood classification algorithms with practical implementation examples
Adaptive PCNN (Pulse-Coupled Neural Network) Fusion
Adaptive PCNN (Pulse-Coupled Neural Network) fusion provides excellent pixel-level image fusion performance, particularly effective for visible light and infrared image integration.
Image Fusion Processing Tool with GUI Interface
Foreign-developed image fusion processing tool with GUI interface including complete source code - beneficial for learning image processing algorithms and interface implementation
MATLAB Image Fusion Toolbox
MATLAB Image Fusion Toolbox: Implementation of Multi-Source Sensor Image Fusion with Advanced Algorithms
Wavelet Transform-Based Image Fusion
Image fusion using wavelet transform with GUI implementation, enabling various image processing operations through an interactive interface.
Research on Image Fusion Technology Based on Compressed Sensing
With the advancement of compressed sensing technology, research on compressed sensing-based image fusion has gained increasing attention. Leveraging the characteristics of image Fourier transform coefficients, this study proposes a compressed sensing domain image fusion algorithm based on high-frequency and low-frequency importance metrics under a dual-star sampling mode. The algorithm begins by acquiring measurements through dual-star sampling, then calculates importance metrics for high- and low-frequency regions as fusion operators, performs weighted fusion of the measurements, and finally reconstructs the fused image by solving a minimum total variation optimization problem. Subjective and objective experimental results demonstrate that this algorithm outperforms other Fourier-based approaches, with implementations involving sparse sampling and convex optimization techniques.
Image Fusion with Enhanced NSCT Algorithms
Application Background: This MATLAB experiment focuses on multi-focus image fusion, utilizing source images such as "pepsi" and "clock". The program has been modified with improvements to high/low-frequency algorithms, ensuring stability for graduation thesis use. Key Technologies: Implements image fusion through point-wise NSCT transformation using the NSCT toolbox, employing maximum pixel method for low-frequency components and maximum variance method for high-frequency components. Enhanced algorithm incorporates pixel correlation-based fusion methodology.