This page contains the materials needed course “Méthodes de séparation de sources pour l’analyse de données en astrophysique” given at ENS Paris-Saclay (Master 2 MVA).
All the necessary material, slides and data are available at https://github.com/BSScourse/MVA
Slides of the courses :
Course #1 : Introduction, unsupervised source separation, statistical principles
Course #2 : Advanced Independent Component Analysis
Course #3 : Unsupervised source separation, the sparse way
Course #4 : Optimisation and proximal algorithms for source separation-take I
Course #5 : Optimisation and proximal algorithms for source separation-take II
Course #6 : Plug & play methods, applications to astrophysics
Course #7 : Non-Negative Matrix Factorisation, theory and practice
Course #8 : Advanced NMF, from linear to non-linear models
References :
Books :
Astronomical Image And Data Analysis, Starck, Murtagh, Springer
Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity, Starck, Murtagh, Fadili, Cambridge University Press
Astronomical Image And Data Analysis, Starck, Murtagh, Springer
Handbook of blind source separation, Comon, Jutten, Academic Presss
Articles :
Sparse representations
The undecimated wavelet decomposition and its reconstruction (more details about the starlet)
Curvelets and ridgelets(all about the curvelet and ridgelet transform)
The curvelet transform for image denoising
Sparse Poisson intensity estimation(wavelet, sparsity for image denoising)
Discussions about the Bayesian interpretation of sparsity :
Sparsity and the Bayesian perspective
Should penalized least squares regression be interpreted as Maximum A Posteriori estimation?
Convex optimization and proximal calculus:
Forward-backward splitting algorithm
Accelerated first-order proximal algorithms
Primal-dual proximal algorithms
Article on reweighted L1 techniques
Independent component analysis:
A Unifying Information-Theoretic Framework for Independent Component Analysis
Sparse blind source separation:
Non-negative matrix factorization:
Sparse NMF, sparse domain, Rapin et al
Dictionary learning:
K-SVD algorithm, application to denoising