{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:38:57Z","timestamp":1760240337978,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T00:00:00Z","timestamp":1557792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Economy, Industry and Competitiveness","award":["MINECO-18-AYA2017-89121-P"],"award-info":[{"award-number":["MINECO-18-AYA2017-89121-P"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Correcting atmospheric turbulence effects in light with Adaptive Optics is necessary, since it produces aberrations in the wavefront of astronomical objects observed with telescopes from Earth. These corrections are performed classically with reconstruction algorithms; between them, neural networks showed good results. In the context of solar observation, the usage of Adaptive Optics on solar differs from nocturnal operations, bringing up a challenge to correct the image aberrations. In this work, a convolutional approach is given to address this issue, considering SCAO configurations. A reconstruction algorithm is presented, \u201cShack-Hartmann reconstruction with deep learning on solar\u2013prototype\u201d (proto-HELIOS), to correct on fixed solar images, achieving an average 85.39% of precision in the reconstruction. Additionally, results encourage to continue working with these techniques to achieve a reconstruction technique for all the regions of the sun.<\/jats:p>","DOI":"10.3390\/s19102233","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T10:42:33Z","timestamp":1557830553000},"page":"2233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations"],"prefix":"10.3390","volume":"19","author":[{"given":"Sergio Luis","family":"Su\u00e1rez G\u00f3mez","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of Oviedo, Calvo Sotelo s\/n, 33007 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9912-7807","authenticated-orcid":false,"given":"Carlos","family":"Gonz\u00e1lez-Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Oviedo, Campus of Viesques s\/n. 33024 Gij\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Garc\u00eda Riesgo","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Oviedo, Calvo Sotelo s\/n, 33007 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4249-1026","authenticated-orcid":false,"given":"Maria Luisa","family":"S\u00e1nchez Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Oviedo, Calvo Sotelo s\/n, 33007 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-2811","authenticated-orcid":false,"given":"Francisco Javier","family":"Iglesias Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Business Administration, University of Oviedo, Avda. del Cristo s\/n, 33006 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2038-4606","authenticated-orcid":false,"given":"Jes\u00fas Daniel","family":"Santos","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Oviedo, Calvo Sotelo s\/n, 33007 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Roddier, F. 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