Log In Sign Up

Pocket Guide to Solve Inverse Problems with GlobalBioIm

by   Emmanuel Soubies, et al.

GlobalBioIm is an open-source MATLAB library for solving inverse problems. The library capitalizes on the strong commonalities between forward models to standardize the resolution of a wide range of imaging inverse problems. Endowed with an operator-algebra mechanism, GlobalBioIm allows one to easily solve inverse problems by combining elementary modules in a lego-like fashion. This user-friendly toolbox gives access to cutting-edge reconstruction algorithms, while its high modularity makes it easily extensible to new modalities and novel reconstruction methods. We expect GlobalBioIm to respond to the needs of imaging scientists looking for reliable and easy-to-use computational tools for solving their inverse problems. In this paper, we present in detail the structure and main features of the library. We also illustrate its flexibility with examples from multichannel deconvolution microscopy.


page 12

page 17


Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems

We study the problem of reconstructing solutions of inverse problems whe...

Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems

Employing deep neural networks as natural image priors to solve inverse ...

Model-Aware Regularization For Learning Approaches To Inverse Problems

There are various inverse problems – including reconstruction problems a...

Immersed Boundary Method for the Complete Electrode Model in Electrical Impedance Tomography

We propose an immersed boundary scheme for the numerical resolution of t...

Deep Learning Techniques for Inverse Problems in Imaging

Recent work in machine learning shows that deep neural networks can be u...

Maxiset point of view for signal detection in inverse problems

This paper extends the successful maxiset paradigm from function estimat...

iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling

U-Nets have been established as a standard neural network design archite...