MATLAB package of iterative regularization methods and large-scale test problems. This software is described in the paper "IR Tools: A MATLAB Package of Iterative Regularization Methods and Large-Scale Test Problems" that will be published in Numerical Algorithms, 2018.

A unifying Matlab framework for the development of reconstruction algorithms (solving inverse problems) in computational imaging

Open-source multi-dimensional tomographic reconstruction software (OMEGA)

Radar and DC resistivity 2.5D multi-physics inversion suite. Forward modeling, separate inversions, joint inversions.

Codes related to the publication Gaussian mixture Markov chain Monte Carlo method for linear seismic inversion

Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems

A Physical Model of Non-stationary Blur in Ultrasound Imaging

Geometric Inverse Problem using Parametric Level-Set

Repository for ISMRM Reproducible Research Study Group Challenge 2019

Real-time EEG source localization based on Smarting mBrainTrain EEG headset and implemented in Matlab.

MATLAB implementation of "Phaseless Low Rank Matrix Recovery and Subspace Tracking", ICML 2019, longer version to appear in IEEE Transactions on Information Theory, 2020.

ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography

Matlab implementation of online and window dynamic mode decomposition algorithms

Plug-and-Play ADMM scheme based on an adaptive denoiser using the Schroedinger equation's solutions of quantum physics.

This repository contains different algorithms for attitude estimation (roll, pitch and yaw angles) from IMU sensors data: accelerometer, magnetometer and gyrometer measurements

Software supporting "Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography" publication in Inverse Problems journal

ARBO is a Matlab/C++ package for simulation and analysis of arbovirus nonlinear dynamics.

A flexible SIM reconstruction method capable to handle difficult data prone to reconstruction artifacts.

Signal and image denoising using quantum adaptive transformation.