Collection of popular and reproducible image denoising works.

Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.

Operator Discretization Library https://odlgroup.github.io/odl/

PDE-Net: Learning PDEs from Data

Python toolkit for modeling and inversion in geophysics. DEPRECATED in favor of our newer libraries (see www.fatiando.org)

🦐 Electromagnetic Simulation + Automatic Differentiation

Python based dashboard for real-time Electrical Impedance Tomography including image reconstruction using Back Projection, Graz Consensus and Gauss Newton methods

PyLops – A Linear-Operator Library for Python

Probabilistic Inference on Noisy Time Series

[CVPR 2020] Official Implementation: "Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models".

Discretization tools for finite volume and inverse problems.

SCIRun is a Problem Solving Environment, for modeling, simulation and visualization of scientific problems. This is version 5, the upgraded version of SCIRun v4.

Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices

Python based toolkit for Electrical Impedance Tomography

Forward modeling, inversion, and processing gravity and magnetic data

Learned Primal-Dual Reconstruction

Solving ill-posed inverse problems using iterative deep neural networks

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

MIRT: Michigan Image Reconstruction Toolbox (Julia version)

Implementation of "Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems"

Collection of popular and reproducible video denoising works.

The set of CPU/GPU optimised regularisation modules for iterative image reconstruction and other image processing tasks

This project hosts the code and datasets I used for Deep Learning course at Boston University. It aims to post-process the images the low quality images produced as a result of solving inverse problems in imaging (particularly Computed Tomography) and produce high-quality images.

Deep neural network to reconstruct Confocal 3D stacks from Light Field Microscopy images.

Code for the article "Learning to solve inverse problems using Wasserstein loss"

Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning

Python for ultrashort laser pulse retrieval

i-RIM applied to the fastMRI challenge data.

Iterative unfolding for Python