Papers › torchmfbd: a flexible multi-object multi-frame blind deconvolution code
torchmfbd: a flexible multi-object multi-frame blind deconvolution code
A. Asensio Ramos, C. Díaz Baso, C. Kuckein, S. Esteban Pozuelo, M. G. Löfdahl
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Post-facto image restoration techniques are essential for improving the quality of ground-based astronomical observations, which are affected by atmospheric turbulence. Multi-object multi-frame blind deconvolution (MOMFBD) methods are widely used in solar physics to achieve diffraction-limited imaging. We present torchmfbd, a new open-source code for MOMFBD that leverages the PyTorch library to provide a flexible, GPU-accelerated framework for image restoration. The code is designed to handle spatially variant point spread functions (PSFs) and includes advanced regularization techniques. The code implements the MOMFBD method using a maximum a-posteriori estimation framework. It supports both wavefront-based and data-driven PSF parameterizations, including a novel experimental approach using non-negative matrix factorization. Regularization techniques, such as smoothness and sparsity constraints, can be incorporated to stabilize the solution. The code also supports dividing large fields of view into patches and includes tools for apodization and destretching. The code architecture is designed to become a flexible platform over which new reconstruction and regularization methods can also be implemented straightforwardly. We demonstrate the capabilities of torchmfbd on real solar observations, showing its ability to produce high-quality reconstructions efficiently. The GPU acceleration significantly reduces computation time, making the code suitable for large datasets. The code is publicly available at https://github.com/aasensio/torchmfbd.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections