Explore MATLAB source code curated for "熵" with clean implementations, documentation, and examples.
The Adaptive Particle Swarm Optimization algorithm improves upon standard PSO by incorporating entropy and average particle distance concepts, significantly accelerating convergence while maintaining global search capabilities. This enhancement reduces susceptibility to local optima and effectively handles complex optimization problems through dynamic parameter adjustments and swarm diversity monitoring.
Quantitative assessment of image fusion outcomes using statistical and information-theoretic metrics including mean, standard deviation, entropy, gradient, correlation coefficient, and spectral distortion
Image Fusion Evaluation Program utilizes multiple metrics including mean, standard deviation, entropy, gradient, correlation coefficient, and spectral distortion to comprehensively assess and compare fused image quality. Implementation involves MATLAB/Python functions for quantitative analysis of fusion performance.
Image sharpness evaluation using standard deviation, spatial frequency threshold, entropy, and Brenner's measure with MATLAB code implementation
Image quality assessment using entropy, average gradient, edge strength, variance, and other metrics with implementation insights
MATLAB implementation for extracting image texture features including variance, entropy, angular second moment, with code demonstrations and algorithm explanations.
Implementation of image variance, skewness, kurtosis, energy, and entropy with simple, executable MATLAB code and algorithm explanations
This comprehensive resource covers extensive tools including correlation dimension analysis, information content measurement, GP algorithm implementations, and entropy calculations. Each problem type is approached with multiple methodologies for comparative analysis, featuring Lorenz system algorithms. Represents curated results from systematic organization efforts.
Bayesian Machine Learning for mutual information calculation, entropy and joint entropy computation. Enhanced version applicable to stochastic inversion with improved uncertainty quantification.
Image processing performance evaluation using metrics including Peak Signal-to-Noise Ratio (PSNR), Entropy, and Mean Square Error (MSE), with implementation approaches for calculating these quantitative measurements