Paper Citation Record · LEDGER

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations

As of 19 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.04781.

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pith.paper-citation-record.v1
2506.04781 v1

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measured 29 of 29 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

Observation cb5621d4-e7ab-494d-80ce-cd19f6ab1f45 · outbound

This paper cites 2018, A&A, 620, A73 Asensio Ramos, A., Esteban Pozuelo, S., & Kuckein, C.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2018, A&A, 620, A73 Asensio Ramos, A., Esteban Pozuelo, S., & Kuckein, C

Reference 1

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Observation 7eb4f844-200b-404d-bb17-6631577e25eb · outbound

This paper cites 2012, Astron.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2012, Astron

Reference 2

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Observation 7b9ba49a-694f-4c28-983a-2dceec66b070 · outbound

This paper cites 2023, JATIS, 9, 015001.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2023, JATIS, 9, 015001

Reference 3

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Observation b50287c5-c8e6-4fc7-ac02-c3068a1e500c · outbound

This paper cites A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms

Reference 4

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Observation 6323b4f5-19ec-493e-9f4e-3b0bf1287489 · outbound

This paper cites 2020, Commun.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2020, Commun

Reference 5

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Observation a422fd11-e789-4ee1-b6c2-045d177ed585 · outbound

This paper cites 2017, in Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (Red Hook, NY , USA: Curran Associates Inc.), 6629–6640.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2017, in Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (Red Hook, NY , USA: Curran Associates Inc.), 6629–6640

Reference 6

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Observation 1403afbb-618f-4535-a607-cb8c16e4c374 · outbound

This paper cites 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 10173.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 10173

Reference 7

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Observation 7cae194b-ab46-4836-92b5-35ef3f9a61f3 · outbound

This paper cites 2017, in 2017 IEEE International Confer- ence on Computer Vision (ICCV), 2458–2467.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2017, in 2017 IEEE International Confer- ence on Computer Vision (ICCV), 2458–2467

Reference 8

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Observation ebc846e0-baa7-4e87-a1ae-d13acdd0a8d1 · outbound

This paper cites an unresolved cited work.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations Unresolved cited work

Reference 9

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Observation 5635def7-b17d-4694-bdfd-a3c47abcfeca · outbound

This paper cites Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning

Reference 10

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Observation b3dd6e00-83c8-4aa8-a154-abc5894165e6 · outbound

This paper cites 2020, ApJ, 903, L25.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2020, ApJ, 903, L25

Reference 11

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Observation 934f2877-8446-4ccd-aa22-727e9bf90674 · outbound

This paper cites 2019, ApJ, 881, L30.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2019, ApJ, 881, L30

Reference 12

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Observation e5f57745-af2e-4b3a-abe0-a3118722956e · outbound

This paper cites 2019, Nat.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2019, Nat

Reference 13

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Observation 71106bd9-6ea3-4196-a57d-ca5ec71128e1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations Adam: A Method for Stochastic Optimization

Reference 14

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Observation c2bf55c5-a8b0-46af-9b6d-792bed314902 · outbound

This paper cites 2020, A&A, 641, A27.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2020, A&A, 641, A27

Reference 15

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Observation 1d84c8d2-d59c-404e-9db9-fc621e90ea75 · outbound

This paper cites 2017, in Fine Structure and Dynam- ics of the Solar Atmosphere, ed.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2017, in Fine Structure and Dynam- ics of the Solar Atmosphere, ed

Reference 16

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Observation b88460c6-3eb2-48ea-95fc-b79a0c53213e · outbound

This paper cites 2017, in 2017 IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), 105–114 Löfdahl, M., Noort, M., & Denker, C.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2017, in 2017 IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), 105–114 Löfdahl, M., Noort, M., & Denker, C

Reference 17

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Observation 35a17c62-517d-4903-a7fb-4dc23945e71e · outbound

This paper cites W., Weigelt, G., & Wirnitzer, B.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations W., Weigelt, G., & Wirnitzer, B

Reference 18

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Observation 5e4bbd99-5c00-4b22-b793-9409ebadbd7d · outbound

This paper cites 2017, in 2017 IEEE International Conference on Computer Vision (ICCV) (Los Alamitos, CA, USA: IEEE Computer Soci- ety), 2813–2821.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2017, in 2017 IEEE International Conference on Computer Vision (ICCV) (Los Alamitos, CA, USA: IEEE Computer Soci- ety), 2813–2821

Reference 19

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Observation 80d6e147-4ab4-4091-b927-d1958d6e26c9 · outbound

This paper cites 2019, ApJ, 884, L23.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2019, ApJ, 884, L23

Reference 20

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Observation f3eebcac-60eb-47ad-8fd1-20b6e40be480 · outbound

This paper cites 2017, in NIPS-W Quintero Noda, C., Schlichenmaier, R., Bellot Rubio, L.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2017, in NIPS-W Quintero Noda, C., Schlichenmaier, R., Bellot Rubio, L

Reference 21

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Observation 86a4eb98-3a0e-43af-93bc-5da1b3943092 · outbound

This paper cites & Marino, J.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations & Marino, J

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:13.539569Z

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Observation 0c48f331-e3fa-4f49-8b5d-965db55bf9ea · outbound

This paper cites R., Warner, M., Keil, S.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations R., Warner, M., Keil, S

Reference 23

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Observation 37d6ea04-fcfd-4dec-a47a-21ce664db37a · outbound

This paper cites 2012, Astronom.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2012, Astronom

Reference 24

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Observation e7f8a594-c861-47b5-8798-c1ed3e4d516b · outbound

This paper cites 2020, ApJ, 895, L16.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2020, ApJ, 895, L16

Reference 25

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Observation cab1fe9f-dd2e-4635-aa1f-884bca974f25 · outbound

This paper cites 2021, ApJ, 920, 101 The SunPy Community, Barnes, W.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2021, ApJ, 920, 101 The SunPy Community, Barnes, W

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:13.439651Z

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Observation 164a9f14-5bf0-4e64-b50d-66b0d7205dec · outbound

This paper cites 2018, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8798–8807.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2018, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8798–8807

Reference 27

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Observation d6dd4d46-eb8b-43dd-bc15-518340cc8641 · outbound

This paper cites 2004, Image Processing, IEEE Transactions on, 600 Wöger, F., von der Lühe, O., & Reardon, K.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations 2004, Image Processing, IEEE Transactions on, 600 Wöger, F., von der Lühe, O., & Reardon, K

Reference 28

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Observation 52a634eb-4591-434a-9fb4-6c875c6f1708 · outbound

This paper cites an unresolved cited work.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations Unresolved cited work

Reference 29

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