Workshop on Sparsity and its application to large inverse problems.
Robinson College, Cambridge University, Cambridge UK.
December 14-15, 2008
Abstract:
The aim of this workshop will be to draw together much of the recent work on algorithms which encourage sparsity, such as the minimisation of cost functions involving Lp-norms (typically 0 <= p < 2), with good methods for solving inverse problems on large datasets such as high-resolution images and 3D data. Usually such problems must be solved iteratively and there is a great need to ensure rapid convergence if the dataset is large, in order to avoid long computation times. Of particular interest are a number of recent papers on fast solutions to L1-minimisation problems and also on iterative threshold reduction methods that allow good solutions to be found to the non-convex L0-minimisation problem. Within the iterative context, it is also possible to adjust the weighting functions for terms in an L2-minimisation so that it approximates an L1 or L0 minimisation process.
In addition to well-known applications such as image deconvolution, there are strong links between this work and the emerging field of compressed sensing. The proposed workshop will discuss the above problem areas and attempt to unify the fairly diverse set of techniques that are currently being used into a more fundamental framework.
Technical Program
Day 1 (Sunday 14/12/08):
Coffee and registration from 10:00
11.15-11:30 Introduction by the organizers
11.30-12.30 Plenary talk 1: Remi Gribonval - Some stories about Lp-minimization, the
Restricted Isometry Property, and excessive pessimism.
Lunch
2:00-3:30 Oral Session
2:00-2:30 N. Kingsbury - Iterative Sparsity Methods for Coding and Deconvolution with
Overcomplete Transforms
2:30-3:00 P. Vandergheynst - Joint sparsity for multimodal signals
3:00-3:30 M. Plumbley - Stagewise Polytope Faces Pursuit for Recovery of Sparse
Representations
3.30-4.30 Tea and poster session (Posters 1-3-5-7-9-11)
4:30-6:00 Oral Session
4:30-5:00 L. Daudet - Divide and conquer: a few tactical approaches for on-the-fly /
parallelized sparse decompositions.
5:00-5:30 T. Blumensath - Generalising Sparsity: A Union of Subspaces Model
5:30-6:00 Y. Eldar - Compressed Sensing of Analog Signals
Dinner at Trinity College from 7:00pm.
Day 2 (Monday 15/12/08):
9.30 - 10.30 Plenary talk 2: Richard Baraniuk - Model-based Compressive Sensing
10.30-11.30 Coffee and poster session (Posters 2-4-6-8-10)
11:30-13:00 Oral Session
11:30-12:00 M. Davies - Fast methods for sparse recovery: alternatives to L1
12:00-12:30 J. Fadili - Monotone Operator Splitting and Fast Sparse Solutions of Inverse
Problems.
12:30-13:00 J. Tanner - The Geometry of Compressed Sensing: A Tale of Three Polytopes
Lunch
2.00-3.00 Plenary talk 3: Christine De Mol - Sparsity-enforcing regularization algorithms
3.00-4.30 Oral session
3:00-3:30 I. Loris - Comparison of algorithms for the minimization of L1-penalized
functionals
3:30-4:00 J. Portilla - Dynamic iterated hard-thresholding for non-convex sparse optimisation
4:00-4:30 R. Molina - Hierarchical Laplace Modelling and Inference in Bayesian
Compressive Sensing
Posters: (All posters should be displayed both days, but odd-numbered posters will be
presented on the 14th and even-numbered posters will be presented on the 15th).
1. Hojjat Akhondi – Single and Multi-channel Sampling of Bilevel Polygons Using
Explonential Splines.
2. Amin Karbasi – The Characterization of the Maximum-Likelihood Support Estimation
at High SNR.
3. Lu Gan - Structurally Random Matrices for Compressed Sensing Related Applications
4. David K. Hammond - Wavelets on Graphs via Spectral Graph Theory
5. Andrew Nesbit - Sparse, Adaptive Signal Decompositions and their Application to
Blind Audio Source Separation
6. Gabriel Rilling - Compressed sensing based compression of SAR raw data
7. Y. Wiaux - Compressed sensing imaging techniques for aperture synthesis by radio
interferometry
8. Adam Scholefield Quadtree Structured Restoration Algorithms for Piecewise
Polynomial Images
9. Zeynep Engin - On the use of sparsity to perform a parallel search for inverse problem
of transformation estimation
10. Mehrdad Yaghoobi - Compressible Dictionary Learning for the Fast Sparse
Approximations
11. Mohammad Golbabaee – Typical Case Analysis for OMP.
