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)

Organizers:

Wenjing Liao (Georgia Institute of Technology)

Haizhao Yang (Duke University)

Zhizhen Zhao (University of Illinois Urbana-Champaign)

Keywords:

computer vision, deep learning, image compression, image reconstruction, image representation, inverse problems, machine learning