Evaluating Image Fusion Results
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
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.
Texture Descriptors Based on Regional Brightness Histograms
Implementation of texture descriptors derived from regional brightness histograms, including mean, standard deviation, smoothness, third moment, uniformity, and entropy - with code-level explanations for algorithm integration
Traditional Modulation Recognition Algorithms
Traditional modulation recognition algorithms utilize key statistical features including: maximum value of the zero-centered normalized instantaneous amplitude spectral density, standard deviation of the zero-centered normalized instantaneous amplitude absolute value, standard deviation of the absolute value of the nonlinear component in zero-centered non-weak signal segment instantaneous phase, standard deviation of the nonlinear component in zero-centered non-weak signal segment instantaneous phase, and standard deviation of the absolute value of zero-centered normalized non-weak signal segment instantaneous frequency. These features can be computationally extracted using signal processing techniques to enhance classification performance.