Collection of popular and reproducible image denoising works.
-
Updated
Apr 2, 2020
{{ message }}
Collection of popular and reproducible image denoising works.
PDE-Net: Learning PDEs from Data
Operator Discretization Library https://odlgroup.github.io/odl/
Python based dashboard for real-time Electrical Impedance Tomography including image reconstruction using Back Projection, Graz Consensus and Gauss Newton methods
So far a single damping factor (epsR and epsI) can be provided to pylops.avo.prestack.PrestackInversion. However as it is known that some parameters are more constrained than others in any AVO linearisation it is useful to provide different damping factors for the different parameters to invert for.
[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.
Describe the bug
If eps_r is shape (N,) then the fields solved are shape (N,1)
Either:
eps_r arrayseps_r shape and reshape the fields to match.Description of the desired feature
On the last community call we decided to move all sample datasets in Fatiando to Rockhound.
Harmonica datasets are being moved to Rockhound on fatiando/rockhound#84.
After it is merged, we should remove the harmonica.datasets along with the data folder. Besides, all gallery examples should download their data from Rockhound, there
Python based toolkit for Electrical Impedance Tomography
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.
MIRT: Michigan Image Reconstruction Toolbox (Julia version)
Collection of popular and reproducible video denoising works.
Implementation of "Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems"
The set of CPU/GPU optimised regularisation modules for iterative image reconstruction and other image processing tasks
Deep neural network to reconstruct Confocal 3D stacks from Light Field Microscopy images.
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.
Code for the article "Learning to solve inverse problems using Wasserstein loss"
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
i-RIM applied to the fastMRI challenge data.
Python for ultrashort laser pulse retrieval
Iterative unfolding for Python
Add a description, image, and links to the inverse-problems topic page so that developers can more easily learn about it.
To associate your repository with the inverse-problems topic, visit your repo's landing page and select "manage topics."
we should add .ipynb to the gitignore - notebooks are out-of-scope for this repo. In the past we have had some notebooks be committed which adds a large volume of code to the log that is later removed