Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Image Fusion: Algorithms and Applications
Image Fusion: Algorithms and Applications
Signal Processing Techniques for Knowledge Extraction and Information Fusion
Signal Processing Techniques for Knowledge Extraction and Information Fusion
Empirical mode decomposition for trivariate signals
IEEE Transactions on Signal Processing
The complex local mean decomposition
Neurocomputing
Multivariate empirical mode decomposition for quantifying multivariate phase synchronization
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
The complex bidimensional empirical mode decomposition
Signal Processing
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
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Empirical mode decomposition (EMD) is a fully data driven technique for decomposing signals into their natural scale components. However the problem of uniqueness, caused by the empirical nature of the algorithm and its sensitivity to changes in parameters, makes it difficult to perform fusion of data from multiple and heterogeneous sources. A solution to this problem is proposed using recent complex extensions of EMD which guarantees the same number of decomposition levels, that is the uniqueness of the scales. The methodology is used to address multifocus image fusion, whereby two or more partially defocused images are combined in automatic fashion so as to create an all in focus image.