Issue |
A&A
Volume 688, August 2024
|
|
---|---|---|
Article Number | A88 | |
Number of page(s) | 15 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202449568 | |
Published online | 08 August 2024 |
Solar multiobject multiframe blind deconvolution with a spatially variant convolution neural emulator
1
Instituto de Astrofísica de Canarias (IAC), Avda Vía Láctea S/N,
38200
La Laguna, Tenerife,
Spain
e-mail: andres.asensio@iac.es
2
Departamento de Astrofísica, Universidad de La Laguna,
38205
La Laguna, Tenerife,
Spain
e-mail: andres.asensio@iac.es
Received:
10
February
2024
Accepted:
15
May
2024
Context. The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth’s atmosphere. Traditional methods of post facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field of view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts.
Aims. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution.
Methods. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multiobject multiframe blind deconvolution algorithm for solar images.
Results. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding the artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude.
Key words: methods: data analysis / methods: numerical / techniques: image processing / Sun: atmosphere
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.