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Utilities and methods to use the PnP algorithm and MACE framework on image reconstruction problems. Includes demos for superresolution and CT.

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gbuzzard/PnP-MACE

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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

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

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).

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Utilities and methods to use the PnP algorithm and MACE framework on image reconstruction problems. Includes demos for superresolution and CT.

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