Principal Component Analysis Effectively Achieves Image Fusion with Detailed Implementation Insights
Principal Component Analysis demonstrates strong performance in image fusion applications, particularly excelling at spatial feature preservation while showing relatively weaker spectral characteristic retention, with implementation involving covariance matrix computation and eigenvalue decomposition.