Digital Image ICA Research Domain
A professional MATLAB package for digital image ICA research, containing source code, test images (data), and result datasets. Supports ICA/ISR/TOPOICA computations with modular algorithm implementations.
ICA as a Novel Approach for Image Watermarking Implementation
ICA represents a relatively new methodology for image watermarking, implemented using MATLAB with capabilities for both watermark embedding and extraction. The implementation typically involves independent component analysis algorithms for feature separation and watermark signal processing.
ICALAB for Signal Processing Toolbox: A Comprehensive Toolkit for Blind Source Separation (BSS)
ICALAB for Signal Processing Toolbox provides implementations for Blind Source Separation (BSS), Independent Component Analysis (ICA), Complex-valued ICA (cICA), Recursive ICA (ICA-R), Sparse Component Analysis (SCA), and Morphological Component Analysis (MCA)
Frequency-Domain Blind Source Separation (BSS) with Parametric Pearson Distribution Modeling
In frequency-domain blind source separation (BSS) for speech signals using independent component analysis (ICA), a practical parametric Pearson distribution system is employed to model the statistical distribution of frequency-domain source components, improving separation accuracy through optimized probability density function matching.
Bayesian Independent Component Analysis Algorithm
This implementation presents a Bayesian ICA algorithm designed for linear instantaneous mixing models with additive Gaussian noise [1]. The inference problem is resolved through ML-II estimation, where sources are determined by integrating over the source posterior distribution while noise covariance and mixing matrix parameters are optimized by maximizing the marginal likelihood [1].
ICA Blind Source Separation
Independent Component Analysis (ICA) and Blind Source Separation with Algorithm Implementation Insights
Blind Source Analysis Based on Negentropy
Negentropy-based Blind Source Separation and Independent Component Analysis (ICA)
NMF and Its Various Successor Methods
NMF and Its Various Evolutionary Approaches with Implementation Insights
Blind Source Separation Based on Negentropy
Blind source separation using negentropy for signal processing applications
Complete Independent Component Analysis (ICA) Package
Comprehensive Independent Component Analysis (ICA) package with full signal processing capabilities