Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Sparsity and Morphological Diversity in Blind Source Separation
IEEE Transactions on Image Processing
Dictionary Learning for Noisy and Incomplete Hyperspectral Images
SIAM Journal on Imaging Sciences
Noniterative Convex Optimization Methods for Network Component Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Recently morphological diversity and sparsity have emerged as new and effective sources of diversity for Blind Source Separation. Based on these new concepts, novelmethods such as Generalized Morphological Component Analysis have been put forward. The latter takes advantage of the very sparse representation of structured data in large overcomplete dictionaries, to separate sources based on their morphology. Building on GMCA, the purpose of this contribution is to describe a new algorithm for hyperspectral data processing. Large-scale hyperspectral data refers to collected data that exhibit sparse spectral signatures in addition to sparse spatial morphologies, in specified dictionaries of spectral and spatial waveforms. Numerical experiments are reported which demonstrate the validity of the proposed extension for solving source separation problems involving hyperspectral data.