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A Hybrid Stochastic Framework for Signal Recovery10 Nov 2020

Pakshal Bohra

Wavelets in harmonic analysis and signal processing27 Oct 2020

Michael Unser

Optimal transport-based metric for single-molecule localization microscopy (SMLM)20 Oct 2020

Pol del Aguila Pla

Inverse Problems with Fourier-Domain Measurements and gTV Regularization:

 uniqueness and reconstruction algorithm22 Sep 2020

Thomas Debarre

Time-dependent deep image prior for dynamic MRI08 Sep 2020

Jaejun Yoo

Shortest Multi-spline Bases for Generalized Sampling03 Aug 2020

Alexis Goujon

Convex Optimization in Infinite Sums of Banach Spaces Using Besov Regularization13 Jul 2020

Benoît Sauty De Chalon

Measuring Complexity of Deep Neural Networks29 Jun 2020

Shayan Aziznejad

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging22 Jun 2020

Fangshu Yang

Space Varying Blurs: Estimation, Identification and Applications18 May 2020

Valentin Debarnot

Matrix factorization and phase retrieval for deep fluorescence microscopy11 May 2020

Jonathan Dong

CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning27 Apr 2020

Harshit Gupta

CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning27 Apr 2020

Laurène Donati

Gibbs Sampling-Based Statistical Inference for Inverse Problems20 Apr 2020

Pakshal Bohra

Rethinking Data Augmentation for Low-level Vision Tasks: A Comprehensive Analysis and A New Strategy "CutBlur"23 Mar 2020

Jaejun Yoo

Robust Reconstruction of Fluorescence Molecular Tomography With An Optimized Illumination Pattern04 Mar 2020

Yan Liu

Solving various domain translation problems using deep convolutional framelets11 Feb 2020

Jaejun Yoo

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

Recent advances in deep learning have shown great potentials in improving the entire medical imaging pipeline, from image acquisition and reconstruction to disease diagnosis. In this talk, I will mainly focus on Magnetic Resonance (MR) image reconstruction and analysis. Firstly, I will introduce my recent study on dynamic MR image reconstruction from highly undersampled k-space data. A CRNN (convolutional recurrent neural network) model will be presented where it models the traditional iterative optimisation process in a learning setting and is able to exploit the spatio-temporal redundancies effectively and efficiently. As a complement, a k-t NEXT (k-t Network with X-f Transform) method will be introduced in which image signals are recovered by alternating the reconstruction process between x-f space and image space in an iterative fashion. Secondly, I will briefly present our recent research on parallel MRI reconstruction, where variable splitting idea is adopted and is modeled in a deep learning framework. Besides, some works on MRI analysis directly from undersampled data will also be presented, including cardiac segmentation and motion estimation, where we showed that prediction directly from undersampled MRI can still achieve accurate performance, potentially enabling fast analysis for MR imaging.

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

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