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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

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

Image-based immunoassays are designed to estimate the proportion of biological cells in a sample that generate a specific kind of particles. These assays are instrumental in biochemical, pharmacological and medical research, and have applications in disease diagnosis. In this talk, I describe the model, inverse problem, functional optimization framework, and algorithmic solution to analyze image-based immunoassays that we presented in [1] and [2]. In particular, I will delve into 1) the radiation-diffusion-adsorption-desorption partial differential equation and a re-parametrization of its solution in terms of convolutional operators, 2) the set up, analysis and algorithmic solution of an optimization problem in Hilbert spaces to recover spatio-temporal information from a single image observation, and 3) the derivation of the proximal operator in function spaces for the non-negative group-sparsity regularizer. After discretization, our work results in a convergent, high-performing algorithm with 25 million optimization variables that requires the entire engineering toolbox of tips and tricks, and was recently incorporated in a commercial product [3]. If time allows, I will introduce our work in [4], in which we use the structure of our algorithm to learn a faster, approximated solver for our optimization problem. [1]: Pol del Aguila Pla and Joakim Jaldén, "Cell detection by functional inverse diffusion and non-negative group sparsity—Part I: Modeling and Inverse Problems", IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5407–5421, 2018. Access at: [2]: Pol del Aguila Pla and Joakim Jaldén, "Cell detection by functional inverse diffusion and non-negative group sparsity—Part II: Proximal optimization and Performance Evaluation", IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5422–5437, 2018. Access at: [3]: Mabtech Iris reader. See product page: [4]: Pol del Aguila Pla, Vidit Saxena, and Joakim Jaldén, "SpotNet – Learned iterations for cell detection in image-based immunoassays", 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Access at:

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

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