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Paper Citation Record · LEDGER

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2411.19158.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.19158 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:31:08.237998Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation f9f3e694-b419-4999-968d-159bebfb2594 · outbound

This paper cites Posterior samples of source galaxies in strong gravitational lenses with score-based priors.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Posterior samples of source galaxies in strong gravitational lenses with score-based priors

Reference 1

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Observation bd1d8f05-bff5-45d6-957b-dfd6b51b6b5f · outbound

This paper cites an unresolved cited work.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 2

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Observation fb6bc4ad-d817-4c36-a314-7420d6e46213 · outbound

This paper cites Aihara, Y.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Aihara, Y

Reference 3

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Observation b3419ecb-1467-46c4-968d-50f89a62a41c · outbound

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 4

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Observation 11482e14-095b-4975-990d-a170c7f17509 · outbound

This paper cites Bertero, P.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Bertero, P

Reference 5

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Observation d88a4adf-b899-45e8-a3ba-4fb07ebea19e · outbound

This paper cites Bottrell, H.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Bottrell, H

Reference 6

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Observation 532a4beb-0b2e-49b8-9a73-a1015d53727a · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 7

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Observation 8f31b7e2-fee9-406d-8cfc-723fe3db9730 · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Diffusion Models Beat GANs on Image Synthesis

Reference 8

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Observation dadfd0ab-e79e-4146-977f-63b3de9d9518 · outbound

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 9

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Observation 6804ba8d-9c45-48f1-9ab9-7b3088c42c59 · outbound

This paper cites SeeingGAN: Galactic image deblurring with deep learning for better morphological classification of galaxies.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions SeeingGAN: Galactic image deblurring with deep learning for better morphological classification of galaxies

Reference 10

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Observation be0c61bd-3e93-432d-8d7c-58a5cf265a1a · outbound

This paper cites Goodfellow, Y.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Goodfellow, Y

Reference 11

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Observation e589354e-1ae2-476e-8022-b53f64313a5b · outbound

This paper cites Diffusion models as plug-and-play priors.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Diffusion models as plug-and-play priors

Reference 12

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Observation e1b51d23-c90b-4315-a45d-be14b9ea80be · outbound

This paper cites Heusel, H.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Heusel, H

Reference 13

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Observation 47d4ebbe-62b6-48f5-a3a0-3d5cb885f2ac · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Denoising Diffusion Probabilistic Models

Reference 14

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Observation 37785d5e-306c-43d4-aff9-2deefd84d7fd · outbound

This paper cites Kim and J.-C.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Kim and J.-C

Reference 15

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Observation 6cd58bc5-6374-4ed5-85fe-7bf82a24a4b0 · outbound

This paper cites Lanusse, R.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Lanusse, R

Reference 16

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Observation 2faae26c-30c2-4281-905e-e42c0711ad34 · outbound

This paper cites Lauritsen, H.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Lauritsen, H

Reference 17

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Observation 72cf5c43-a237-4d14-a55f-8ee26b4ecd97 · outbound

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 18

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Observation 07e7fb06-c649-415c-a81c-2110fea71f2a · outbound

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 19

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Observation 64eb793d-b0dc-49d3-bf0c-237a80cb4335 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 20

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Observation fedbfde8-1234-4bba-8d53-fcce14cfe9bf · outbound

This paper cites Michalewicz, M.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Michalewicz, M

Reference 21

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Observation 0bb297f8-d1d3-4e14-80ed-aa85f3669670 · outbound

This paper cites Nelson, V.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Nelson, V

Reference 22

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Observation 13b399ae-4760-46c3-9e6a-37d6aeef6dce · outbound

This paper cites Improved Denoising Diffusion Probabilistic Models.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Improved Denoising Diffusion Probabilistic Models

Reference 23

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 24

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

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This paper cites Ntampaka, C.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Ntampaka, C

Reference 26

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

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This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 28

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Schawinski, C

Reference 30

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Observation fdc9c7c8-50dd-4935-b5bb-98d05e886c47 · outbound

This paper cites Scoville, H.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Scoville, H

Reference 31

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Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 32

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This paper cites Denoising Diffusion Implicit Models.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Denoising Diffusion Implicit Models

Reference 33

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Source-reported events for the cited work

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This paper cites Song and S.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Song and S

Reference 34

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Source-reported events for the cited work

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Observation 94ca63bd-b14e-4759-a346-5a731c7c6b19 · outbound

This paper cites Improved Techniques for Training Score-Based Generative Models.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Improved Techniques for Training Score-Based Generative Models

Reference 35

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Unavailable: canonical work link unavailable.

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Observation 092c9f16-db48-42fb-afb6-d14044aec9bc · outbound

This paper cites an unresolved cited work.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-12T10:31:08.525422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6e923878-6ec8-4fec-b31d-3675045ced96 · outbound

This paper cites Starck, E.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Starck, E

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:31:08.510351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ff620d85-e364-4df2-b6eb-e90544e02c67 · outbound

This paper cites an unresolved cited work.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-12T10:31:08.494234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T10:31:08.219238Z digest=sha256:01849b4fb79de0a13a99cc13cd3cc1952fae74888951971acb43423dbde94bb2

Observation f5c41c8f-b554-46bb-9331-179007671c19 · outbound

This paper cites Tanaka, J.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Tanaka, J

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:31:08.479039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f15e8fe6-60a8-4ecc-bd4d-c3569d64181f · outbound

This paper cites an unresolved cited work.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-12T10:31:08.463336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T10:31:08.227774Z digest=sha256:3c1be13c93046309794a7f193ff9ed0fbb768de034a9a487edd23d88486e6c3a

Observation c8cd8fff-29e4-4b2a-b308-295f8b032159 · outbound

This paper cites V ojtekova, M.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions V ojtekova, M

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:31:08.448662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T10:31:08.232283Z digest=sha256:7dfa89d5f4fe918b1dc573403323af5f161c0ed04a8a50793b0b6846e5bb8dae

Observation 7580a40c-644a-4e59-b31b-6874e840b532 · outbound

This paper cites an unresolved cited work.

Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-12T10:31:08.433273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-12T10:31:08.237998Z digest=sha256:f0b5f432e3e22745595f0a9ea117cd68cbe92a41ce70b19d274b386a6dd46666

Pith citing papers

No inbound Pith citation observations are available.