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This paper focuses on fusing hyperspectral and multispectral images with an unknown arbitrary point spread function (PSF). Instead of obtaining the fused image based on the estimation of the PSF, a novel model is proposed without intervention of the PSF under Bayesian framework, in which the fused image is decomposed into double subspace-constrained matrix-factorization-based components and residuals...
To extract two-dimensional principal components from image samples while being insensitive to outliers, we propose a robust model for two-dimensional principal component analysis (robust 2D-PCA) by regularizing sparse penalty term. Moveover, we develop a novel iterative algorithm for robust 2D-PCA via alternating optimization, learning the projection matrices by bi-directional decomposition. To further...
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