In this paper, principal component analysis based on the Laplacian pyramid image fusion of theory and method: First, the original image, respectively Laplacian pyramid decomposition, and then were used for high-frequency part of the principal component analysis (PCA) fusion method for The average gradient method using low-frequency part of the fusion, the last of the Laplacian pyramid to do the inverse transform to get the final fused image. Through the integration of visual and infrared images, as well as the results of image fusion of different focal length of the algorithm than the simple PCA and Laplacian image fusion can get more useful information with the integration of high-contrast images

PCAAlgorithm.kdh

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