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Block Distributed Majorize-Minimize Memory Gradient Algorithm

This repository contains the Python implementation of the Block Distributed Majorize-Minimize Memory Gradient Algorithm applied to the problem of 3D images restoration potentially handling a depth-variant blur.

Deblurring

Prerequisites and installation

These instructions will get you a copy of the BD3MG, BP3MG and 3MG algorithms running on your multi-processor local or remote Unix machine. The two first algorithms are parallelized on available machine cores. They use the python multiprocessing library in order to handle process distribution of the computations. If you want to get the code working on other distributions (Windows and MacOs), you might need to change process afinity handling in the code.

This version of the algorithms runs on Python (>=3.5) with common libraries listed in the requirements.txt file.

Installing

In order to run the different optimization frameworks in the package, follow the steps below on your command line:

git clone https://github.com/mathieuchal/BD3MG/.git
cd BD3MG
pip install -r requirements.txt

Testing

To ensure that the algorithm functions well on your machine, you can try the synthetic deblurring and denoising problem proposed in the package by entering the following commands.

cd BD3MG 
python BD3MG/test_asynch.py

The code should start with initializing a synthetic 3D blurry and noisy image with Create blurry and noisy image size image: Nx = 128, Ny = 128, Nz = 24 size kernel: Nx = 5, Ny = 5, Nz = 11 ...

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The code implementation of the paper : Block distributed 3MG Algorithm and its application to 3D image restoration

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