Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Atomic Decomposition by Basis Pursuit
SIAM Review
Guest editorial: Image fusion: Advances in the state of the art
Information Fusion
Remote sensing image fusion using the curvelet transform
Information Fusion
Image Fusion: Algorithms and Applications
Image Fusion: Algorithms and Applications
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Pixel-level image fusion with simultaneous orthogonal matching pursuit
Information Fusion
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
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In image fusion techniques based on joint sparse representation (JSR), the composite image is calculated from the fusion of features, which are represented with sparse coefficients. Orthogonal matching pursuit (OMP) and basis pursuit (BP) are the main candidates to estimate the coefficients. Previously OMP is utilized for the advantage of low complexity. However, noticeable errors occur when the dictionary of JSR cannot ensure the coefficients are sparse enough. Alternatively, BP is more robust than OMP in such cases (though suffered from larger complexity). Unfortunately, it has never been studied in image fusion tasks. In this paper, we investigate JSR based on BP for image fusion. The target is to verify that 1) to what extent can BP outperform OMP; and 2) what is the trade-off between BP and OMP. Finally, we conclude, in some cases, fusion with BP obviously outperforms the one with OMP under an affordable computational complexity.