A multi-spectral image fusion algorithm based on PCA and red-black wavelet

A multi-spectral image fusion algorithm based on red-black wavelet (RBW) and principal component analysis (PCA) is proposed in this paper to enhance the image performance. The PCA algorithm was used to extract the diverse features and details of the multi-spectral image, and, then, these features were decomposed by RBW and fused by the improved diverse algorithms using low-frequency and high-frequency coefficients at different scales, frequency domains, decomposition layers, and frequency bands. Finally, these fused features and multi-spectral images were reconstructed by RBW and PCA inversion. The results of our experiments showed that the proposed algorithm provided higher spatial resolution and more excellent spectral information. In addition, it improved the processing speed, took less memory, and offers the potential for real-time processing. © 2016 SERSC.