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Adaptive regularization for three-dimensional optical diffraction tomography17 Dec 2019

Thanh-An Pham

About the use of non-imaging data to improve domain adaptation for spinal cord segmentation on MRI26 Nov 2019

Benoît Sauty De Chalon

Lagrangian Tracking of Bubbles Entrained by a Plunging Jet19 Nov 2019

Alexis Goujon

Multigrid Methods for Helmholtz equation and its application in Optical Diffraction Tomography05 Nov 2019

Tao Hong
Department of Computer Science, Technion – Israel Institute of Technology

Efficient methods for solving large scale inverse problems17 Oct 2019

Eran Treister
Computer Science Department at Ben Gurion University of the Negev, Beer Sheva, Israel

Generating Sparse Stochastic Processes24 Sep 2019

Leello Tadesse Dadi

Sparse signal reconstruction using variational methods with fractional derivatives10 Sep 2019

Stefan Stojanovic

Multivariate Haar wavelets and B-splines13 Aug 2019

Tanya Zaitseva

Deep Learning for Magnetic Resonance Image Reconstruction and Analysis06 Aug 2019

Chen Qin

The Interpolation Problem with TV(2) Regularization30 Jul 2019

Thomas Debarre

Duality and Uniqueness for the gTV problem.23 Jul 2019

Quentin Denoyelle

An Introduction to Convolutional Neural Networks for Inverse Problems in Imaging09 Jul 2019

Harshit Gupta

Multiple Kernel Regression with Sparsity Constraints18 Jun 2019

Shayan Aziznejad

Optimal Spline Generators for Derivative Sampling18 Jun 2019

Shayan Aziznejad

Total variation minimization through Domain Decomposition28 May 2019

Vasiliki Stergiopoulou

Cell detection by functional inverse diffusion and non-negative group sparsity07 May 2019

Pol del Aguila Pla
KTH Royal Institute of Technology, Division of Information Science and Engineering, School of Electrical Engineering and Computer Science

Can neural networks always be trained? On the boundaries of deep learning06 May 2019

Matt J. Colbrook
Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge

Measure Digital, Reconstruct Analog16 Apr 2019

Julien Fageot
Harvard Harvard John A. Paulson School of Engineering and Applied Sciences

Deep Learning for Non-Linear Inverse Problems02 Apr 2019

Fangshu Yang

Numerical Investigation of Continuous-Domain Lp-norm Regularization in Generalized Interpolation19 Feb 2019

Pakshal Bohra

Inner-Loop-Free ADMM for Cryo-EM15 Jan 2019

Laurène Donati

Fast PET reconstruction: the home stretch11 Dec 2018

Michael McCann

Self-Supervised Deep Active Accelerated MRI27 Nov 2018

Kyong Hwan Jin

Minimum Support Multi-Splines20 Nov 2018

Shayan Aziznejad

Sparse Coding with Projected Gradient Descent for Inverse Problems 23 Oct 2018

Thanh-an Pham

Adversarially-Sandwiched VAEs for Inverse Problems02 Oct 2018

Harshit Gupta

PSF-Extractor: from fluorescent beads measurements to continuous PSF model11 Sep 2018

Emmanuel Soubies

Analysis of Planar Shapes through Shape Dictionary Learning with an Extension to Splines28 Aug 2018

Anna Song

Complex-order scale-invariant operators and self-similar processes21 Aug 2018

Arash Amini
Sharif University, Tehran, Iran

Variational Framework for Continuous Angular Refinement and Reconstruction in Cryo-EM14 Aug 2018

Mona Zehni

Looking beyond Pixels: Theory, Algorithms and Applications of Continuous Sparse Recovery07 Aug 2018

Hanjie Pan
Audiovisual Communications Laboratory (LCAV, EPFL

Sparse recovery is a powerful tool that plays a central role in many applications. Conventional approaches usually resort to discretization, where the sparse signals are estimated on a pre-defined grid. However, the sparse signals do not line up conveniently on any grid in reality. We propose a continuous-domain sparse recovery technique by generalizing the finite rate of innovation (FRI) sampling framework to cases with non-uniform measurements. We achieve this by identifying a set of unknown uniform sinusoidal samples (which are related to the sparse signal parameters to be estimated) and the linear transformation that links the uniform samples of sinusoids to the measurements. It is shown that the continuous-domain sparsity constraint can be equivalently enforced with a discrete convolution equation of these sinusoidal samples. Then, the sparse signal is reconstructed by minimizing the fitting error between the given and the re-synthesized measurements (based on the estimated sparse signal parameters) subject to the sparsity constraint. Further, we develop a multi-dimensional sampling framework for Diracs in two or higher dimensions with linear sample complexity. This is a significant improvement over previous methods, which have a complexity that increases exponentially with space dimension. An efficient algorithm has been proposed to find a valid solution to the continuous-domain sparse recovery problem such that the reconstruction (i) satisfies the sparsity constraint; and (ii) fits the given measurements (up to the noise level). We validate the flexibility and robustness of the FRI-based continuous-domain sparse recovery in both simulations and experiments with real data in radioastronomy, acoustics and microscopy.

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