Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
High-order contrasts for independent component analysis
Neural Computation
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
Source separation in astrophysical maps using independent factor analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Bayesian separation of images modeled with MRFs using MCMC
IEEE Transactions on Image Processing
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
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It is a recurrent issue in astronomical data analysis that observations are incomplete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are nonstationary processes over the sky. All these effects impair data processing techniques which work in the Fourier domain. Spectral matching ICA (SMICA) is a source separation method based on spectral matching in Fourier space designed for the separation of diffuse astrophysical emissions in cosmic microwave background observations. This paper proposes an extension of SMICA to the wavelet domain and demonstrates the effectiveness of wavelet-based statistics for dealing with gaps in the data.