Introduction

This python package provides methods and utilities to explore the PnP algorithm and MACE framework in the context of image reconstruction problems, along with some simple demos. The ideas leading to this package are outlined in

• A SIAM News overview article from March 2021
• The 2020 SIAM Imaging Sciences Best Paper Award lecture

with more detail in the papers in the references.

Documentation on this package is available at https://pnp-mace.readthedocs.io, which includes installation instructions.

Demo files are available as python scripts and Jupyter notebooks. See the Demos page for more details.

Features

• Implementation of basic Plug-and-Play method as described in

  Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation, *IEEE Transactions on Computational Imaging*, vol. 2, no. 4, Dec. 2016.

• Implementation of the solution of MACE equations using Mann iterations as in

  Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium, *SIAM Journal on Imaging Sciences*, vol. 11, no. 3, pp. 2001-2020, Sept. 2018.

• Demo code illustrating reconstruction with image subsampling and tomography.

This is free software with a BSD license.

Installation and Demos

From a terminal in linux or MacOS, change to the dev_scripts directory, and enter:

source clean_install_all.sh
conda activate pnp_mace

This will create and activate a conda environment called pnp_mace.

Then change to the demo directory and and enter:

python ct_mace.py

(or use any of the other .py files in the demo directory).