Metadata-Version: 2.1
Name: patch-denoise
Version: 1.2.1.dev22
Summary: Denoising method for sequence of images or volumes. Primarly targeting fMRI data.
Author-email: Pierre-antoine Comby <pierre-antoine.comby@crans.org>
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: tqdm
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: ruff ; extra == 'dev'
Provides-Extra: doc
Requires-Dist: pydata-sphinx-theme ; extra == 'doc'
Requires-Dist: numpydoc ; extra == 'doc'
Requires-Dist: sphinx-gallery ; extra == 'doc'
Requires-Dist: sphinx (<5) ; extra == 'doc'
Provides-Extra: optional
Requires-Dist: modopt ; extra == 'optional'
Requires-Dist: nipype ; extra == 'optional'
Requires-Dist: numba ; extra == 'optional'
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'
Requires-Dist: pytest-xdist ; extra == 'test'
Requires-Dist: pytest-sugar ; extra == 'test'

========================
Patch Denoising Methods
========================
.. list-table:: 
   :widths: 25 25 25
   :header-rows: 0

   * -  .. image:: https://img.shields.io/codecov/c/github/paquiteau/patch-denoising
         :target: https://app.codecov.io/gh/paquiteau/patch-denoising
     - .. image:: https://github.com/paquiteau/patch-denoising/workflows/CI/badge.svg
     -  .. image:: https://github.com/paquiteau/patch-denoising/workflows/CD/badge.svg
   * -  .. image:: https://img.shields.io/badge/style-black-black
     -  .. image:: https://img.shields.io/badge/docs-Sphinx-blue
         :target: https://paquiteau.github.io/patch-denoising
     -  Release (TBA)
     

This repository implements patch-denoising methods, with a particular focus on local-low rank methods.

The target application is functional MRI thermal noise removal, but this methods can be applied to a wide range of image modalities.

It includes several local-low-rank based denoising methods:

1. MP-PCA
2. Hybrid-PCA
3. NORDIC
4. Optimal Thresholding
5. Raw Singular Value Thresholding

A mathematical description of theses methods is available in the documentation.



Installation
============

patch-denoise requires Python>=3.8


Quickstart
==========

After installing you can use the ``patch-denoise`` command-line.

.. code::

   $ patch-denoise input_file.nii output_file.nii --mask="auto"

See ``patch-denoise --help`` for detailled options.

Documentation and Examples
==========================

Documentation and examples are available at https://paquiteau.github.io/patch-denoising/


Development version
===================

.. code::

   $ git clone https://github.com/paquiteau/patch-denoising
   $ pip install -e patch-denoising[dev,doc,test,optional]
