Computational Imaging with the Julia Language

Files related to Jeff Fessler's tutorial presentation at the 2019 IMA Workshop on Computational Imaging

The full URL for this directory is
http://web.eecs.umich.edu/~fessler/papers/files/talk/19/ima-julia/
For examples of MRI reconstruction using Julia, see these demos from my ISBI 2019 tutorial and also see the corresponding details in my recent survey paper on optimization methods for MRI reconstruction.
Also of interest might be this URL that describes machine learning with Julia: https://tinyurl.com/ml2-18-jf
The slides (pdf)
The Julia code
To get started with Julia for computational imaging, go to https://github.com/JeffFessler/MIRT.jl and see the README there and the doc/ directory.
Here are step by step instructions for installing Julia and MIRT.

Abstract: Julia is a relatively new programming language that combines many of the best features of languages like Python and tools like Matlab. One interacts with Julia like other high-level scripting languages, e.g., through Jupyter notebooks, yet Julia has excellent computational performance because it is built on top of the LLVM compiler. This tutorial will illustrate some of language features in the context of implementing computational imaging applications like image denoising and image super-resolution. Attendees who want follow along interactively during the tutorial are encouraged to follow the installation instructions at http://web.eecs.umich.edu/~fessler/papers/files/talk/19/ima-julia/ in advance of the session.