Paper Citation Record · LEDGER

Super-resolution of Omnidirectional Images Using Adversarial Learning

As of 19 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:1908.04297.

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

pith.paper-citation-record.v1
1908.04297 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:52:22.413105Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d7bdf50-24b0-4aca-a909-faef4593a9c8 · outbound

This paper cites Algorithm descriptions of pro- jection format conversion and video quality metrics in 360lib,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Algorithm descriptions of pro- jection format conversion and video quality metrics in 360lib,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.910526Z

Source-reported events for the cited work

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Observation e336e52f-7a40-47a3-8992-880ec67a5ef2 · outbound

This paper cites Towards generating ambisonics using audio-visual cue for virtual reality,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Towards generating ambisonics using audio-visual cue for virtual reality,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.898486Z

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Observation 1b972e15-9e2a-400c-9783-b5747e24602f · outbound

This paper cites Visual attention-aware omnidirectional video streaming using optimal tiles for virtual reality,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Visual attention-aware omnidirectional video streaming using optimal tiles for virtual reality,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.884449Z

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Observation e27d050c-2407-440a-8363-d73683b2e40e · outbound

This paper cites Viewport-aware adaptive 360◦ video streaming using tiles for virtual reality,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Viewport-aware adaptive 360◦ video streaming using tiles for virtual reality,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.870586Z

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Observation f82d4ed5-ef0f-49f1-9c96-825e8ade2ec7 · outbound

This paper cites V oronoi-based objective quality metrics for omnidirectional video,.

Super-resolution of Omnidirectional Images Using Adversarial Learning V oronoi-based objective quality metrics for omnidirectional video,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.858214Z

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Observation 4ba450b1-3be3-438c-9549-d86b601959a5 · outbound

This paper cites Toward low-latency and ultra-reliable virtual reality,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Toward low-latency and ultra-reliable virtual reality,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.844673Z

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Observation 6a38ecd1-a3ac-4343-97d0-911fb2c34651 · outbound

This paper cites Plenoptic based super-resolution for omnidirectional image sequences,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Plenoptic based super-resolution for omnidirectional image sequences,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.831250Z

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Observation 8586b941-1813-41fb-bc91-63e6f5bb56f6 · outbound

This paper cites Joint registration and super- resolution with omnidirectional images,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Joint registration and super- resolution with omnidirectional images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.818610Z

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Observation a8f074c4-285c-4e8a-9292-b155c936262a · outbound

This paper cites Learning-based tone mapping operator for efﬁcient image matching,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Learning-based tone mapping operator for efﬁcient image matching,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.806269Z

Source-reported events for the cited work

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Observation ba7b834a-fe4c-4781-8a65-58b41d7fc7ed · outbound

This paper cites Optimizing tone mapping operators for keypoint detection under illumination changes,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Optimizing tone mapping operators for keypoint detection under illumination changes,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.793380Z

Source-reported events for the cited work

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Observation 325f871e-4f00-49ef-945d-c3d43ea2b398 · outbound

This paper cites Learning-Based Tone Mapping Operator for Image Matching,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Learning-Based Tone Mapping Operator for Image Matching,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.780873Z

Source-reported events for the cited work

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Observation 52ded139-c64b-49e0-9b16-4f095232ec87 · outbound

This paper cites The 2018 PIRM Challenge on Perceptual Image Super-resolution.

Super-resolution of Omnidirectional Images Using Adversarial Learning The 2018 PIRM Challenge on Perceptual Image Super-resolution

Reference 12

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Observation 82083766-841f-4b85-bd42-980cf9b84f4d · outbound

This paper cites Learning a single convo- lutional super-resolution network for multiple degradations,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Learning a single convo- lutional super-resolution network for multiple degradations,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.767166Z

Source-reported events for the cited work

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Observation cba74549-973f-4bd1-816c-aa332085d216 · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Photo-realistic single image super-resolution using a generative adversarial network,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.753862Z

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Observation 9315a173-bc7f-485d-a348-53700e562f8d · outbound

This paper cites ESRGAN: Enhanced Super-Resolution generative adversarial networks,.

Super-resolution of Omnidirectional Images Using Adversarial Learning ESRGAN: Enhanced Super-Resolution generative adversarial networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.740853Z

Source-reported events for the cited work

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Observation 3dd06d07-e700-40a1-8265-42321bad10ed · outbound

This paper cites The relativistic discriminator: a key element missing from standard GAN.

Super-resolution of Omnidirectional Images Using Adversarial Learning The relativistic discriminator: a key element missing from standard GAN

Reference 16

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Observation 5ebc877a-df9d-4a0e-9060-3cedde3d5add · outbound

This paper cites Image-to- image translation with conditional adversarial networks,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Image-to- image translation with conditional adversarial networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.727944Z

Source-reported events for the cited work

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Observation 35782d45-8398-4d88-8577-47e321090bd3 · outbound

This paper cites Super-resolution: a com- prehensive survey,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Super-resolution: a com- prehensive survey,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.714933Z

Source-reported events for the cited work

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Observation e452f7ba-ceec-45f4-a7aa-bbdd47116c1a · outbound

This paper cites Deep Learning for Image Super-resolution: A Survey.

Super-resolution of Omnidirectional Images Using Adversarial Learning Deep Learning for Image Super-resolution: A Survey

Reference 19

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Observation a3869eac-93d3-4c50-9582-6822d44f7e31 · outbound

This paper cites Feature learning for the image retrieval task,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Feature learning for the image retrieval task,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.701266Z

Source-reported events for the cited work

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Observation db2bb7b7-145c-4752-93b9-8654779ba5a6 · outbound

This paper cites Colornet - estimating colorfulness in natural images,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Colornet - estimating colorfulness in natural images,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.687948Z

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.

Observation f341acf9-ea5c-4a4f-a62d-3cfb54e32fd2 · outbound

This paper cites Weighted- to-spherically-uniform ssim objective quality evaluation for panoramic video,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Weighted- to-spherically-uniform ssim objective quality evaluation for panoramic video,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.674270Z

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.

Observation 175a990d-2e12-4c2f-819e-badc665c1a74 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Image quality assessment: from error visibility to structural similarity,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.661069Z

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.

Observation 7325f854-b9ee-4dc8-8d08-5cdc28a7b3f0 · outbound

This paper cites AHG8: WS-PSNR for 360 video objective quality evaluation,.

Super-resolution of Omnidirectional Images Using Adversarial Learning AHG8: WS-PSNR for 360 video objective quality evaluation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.648730Z

Source-reported events for the cited work

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Observation 7e5b6eb7-6c59-469e-953d-7f52aca51232 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Very deep convolutional networks for large-scale image recognition,

Reference 25

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Observation e4b74af5-711d-4fe0-8b68-c5447665f506 · outbound

This paper cites Generative adversarial nets,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Generative adversarial nets,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.628446Z

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.

Observation 7a9b5b28-65cc-49b0-82c0-12c729ab5adb · outbound

This paper cites Recogniz- ing scene viewpoint using panoramic place representation,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Recogniz- ing scene viewpoint using panoramic place representation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.616521Z

Source-reported events for the cited work

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Observation 11f2b8e9-1887-4459-a8ce-afe18da251d3 · outbound

This paper cites Weighted-to-spherically-uniform quality evaluation for omnidirectional video,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Weighted-to-spherically-uniform quality evaluation for omnidirectional video,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.603688Z

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.

Observation f40f75bb-e8c3-4857-944b-69e1c0031542 · outbound

This paper cites Saliency-driven omnidirectional imaging adaptive coding: Modeling and assess- ment,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Saliency-driven omnidirectional imaging adaptive coding: Modeling and assess- ment,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.590261Z

Source-reported events for the cited work

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Observation 59243b61-db8b-4368-bb41-1ca254a381ae · outbound

This paper cites Subjective panoramic video quality assessment database for coding applications,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Subjective panoramic video quality assessment database for coding applications,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.576292Z

Source-reported events for the cited work

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Observation ed7fba6c-b7ac-4a5f-9c65-136e57ab37a9 · outbound

This paper cites A Large- Scale compressed 360-degree spherical image database: From subjective quality evaluation to objective model comparison,.

Super-resolution of Omnidirectional Images Using Adversarial Learning A Large- Scale compressed 360-degree spherical image database: From subjective quality evaluation to objective model comparison,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.561669Z

Source-reported events for the cited work

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Observation a152eea2-79d4-4d72-917f-a69f33bc4a7b · outbound

This paper cites Automatic differentiation in PyTorch,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Automatic differentiation in PyTorch,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.548500Z

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.

Observation 0ac4fa00-86e1-4ae0-a88c-f8fbf0c26b80 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Super-resolution of Omnidirectional Images Using Adversarial Learning Adam: A Method for Stochastic Optimization

Reference 33

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Source-reported events for the cited work

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Observation ce3ee2af-a6ee-49aa-9d0f-3adaa1e3b669 · outbound

This paper cites Graph-cut-based model for spectral- spatial classiﬁcation of hyperspectral images,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Graph-cut-based model for spectral- spatial classiﬁcation of hyperspectral images,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.534910Z

Source-reported events for the cited work

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Observation fd062b32-39a9-455c-b5bb-35e8ccb45f20 · outbound

This paper cites An evaluation of hdr image matching under extreme illumination changes,.

Super-resolution of Omnidirectional Images Using Adversarial Learning An evaluation of hdr image matching under extreme illumination changes,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.520535Z

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.

Observation 58a64649-08fd-40f9-9a84-86ac271928ae · outbound

This paper cites Learning-based Adap- tive Tone Mapping for Keypoint Detection,.

Super-resolution of Omnidirectional Images Using Adversarial Learning Learning-based Adap- tive Tone Mapping for Keypoint Detection,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:52:22.505542Z

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.

Pith citing papers

No inbound Pith citation observations are available.