Low dimensional structures in imaging scienceMS42

Many objects of interest in imaging science exhibit a low-dimensional structure, which could mean, for instance, low sparsity of a vector, low-rank property of a large matrix, or low-dimensional manifold model for a data set. Many successful methods rely on deep understanding and clever exploitation of such low-dimensional structures. The goal of this mini-symposium is to bring together researchers actively working on imaging techniques based on low-dimensional models, and to explore some recent state-of-the-art work in scientific computation, machine learning and optimization related with imaging science.

Using invariant features for multi-reference alignment and multi-segment reconstruction
Zhizhen Zhao (University of Illinois Urbana-Champaign)
Model stability of low complexity priors
Samuel Vaiter (IMB, Université de Bourgogne)
Nonconvex Blind Deconvolution: Geometry and Efficient Methods
Yuqian Zhang (Columbia University)
A tale of two bases: local-nonlocal regularization on image patches with convolution framelets
Tingran Gao (The University of Chicago)
Composition-aware spectroscopic tomography
Yoram Bresler (University of Illinois at Urbana-Champaign)
Multiscale vector quantization
Lorenzo Rosasco (University of Genoa, Istituto Italiano di Tecnologia; Massachusetts Institute of Technology)
Steerable graph-Laplacian filters for image-valued manifolds
Boris Landa (Tel Aviv University)
Regularization by invariant multiscale statistics
Ivan Dokmanic (University of Illinois at Urbana–Champaign)
Super-resolution, subspace methods and conditioning of Vandermonde matrices
Wenjing Liao (Georgia Institute of Technology)
An analysis of the BLASSO method for the multi-dimensional super-resolution problem
Clarice Poon (University of Cambridge)
PET-MRI Joint Reconstruction by Joint Sparsity Based Tight Frame Regularization
Jae Kyu Choi (Institute of Natural Sciences, Shanghai Jiao Tong University)
Wenjing Liao (Georgia Institute of Technology)
Haizhao Yang (Duke University)
Zhizhen Zhao (University of Illinois Urbana-Champaign)
computer vision, deep learning, image compression, image reconstruction, image representation, inverse problems, machine learning