Metadata-Version: 2.2
Name: torchmfbd
Version: 0.1
Summary: Multi-frame blind deconvolution with PyTorch
Author-email: "A. Asensio Ramos" <andres.asensio@iac.es>
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: einops>=0.8.0
Requires-Dist: matplotlib>=3.7.5
Requires-Dist: numpy>=1.24.4
Requires-Dist: nvitop>=1.4.2
Requires-Dist: pyyaml>=6.0.2
Requires-Dist: scikit-learn>=1.3.2
Requires-Dist: scipy>=1.10.1
Requires-Dist: tqdm>=4.67.1
Requires-Dist: torch>=2.6.0
Requires-Dist: torchvision>=0.21.0

# torchmfbd

## Introduction
``torchmfbd`` is a Python 3 package to carry out multi-object multi-frame blind deconvolution (MOMFBD) of point-like or 
extended objects, specially taylored for solar images. It is built on top of PyTorch and provides a high-level interface for adding observations,
defining phase diversity channels and adding regularization. It can deal with spatially variant PSFs either by mosaicking the images or by
defining a spatially variant PSF.


## Features

- User-friendly API.
- Easy to use configuration file.
- Spatially invariant and variant PSFs.
- Easy-to-use regularization. The current version supports smooth solutions, and solutions based on the :math:`\ell_1` penalization of the isotropic
undecimated wavelet transform of the object. Regularizations are easily extendable.
- Phase diversity.

## Installation

Install it using ``pip install torchmfbd``.

## Documentation

Visit the [documentation](https://aasensio.github.io/torchmfbd/) for detailed instructions of installation and use.
