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Paper Citation Record · LEDGER

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields

As of 13 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2411.16172.

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

pith.paper-citation-record.v1
2411.16172 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:32:57.758092Z

measured 42 of 42 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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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 8fcd5592-cf24-4496-af9f-6b1f0b076c70 · outbound

This paper cites Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields

Reference 1

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Observation 4f3a6268-0cdd-48b7-90c6-651b7f6ddd1e · outbound

This paper cites Mip-nerf 360: Unbounded anti-aliased neural radiance fields.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Mip-nerf 360: Unbounded anti-aliased neural radiance fields

Reference 2

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Observation d836ef34-a789-4ebc-813e-fab89b5baeb9 · outbound

This paper cites A spatial processor model for ob- ject colour perception.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields A spatial processor model for ob- ject colour perception

Reference 3

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Observation 1e652abe-3948-467f-adeb-5fec3b677c4c · outbound

This paper cites Unsupervised and untrained underwater im- age restoration based on physical image formation model.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Unsupervised and untrained underwater im- age restoration based on physical image formation model

Reference 4

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Observation 3761ecec-9243-4747-af95-5706d8bee8f7 · outbound

This paper cites Nerv: Neural representations for videos.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Nerv: Neural representations for videos

Reference 5

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Observation de233bde-6a50-4bd3-a37a-ae76b11d530f · outbound

This paper cites Aug-nerf: Training stronger neural radiance fields with triple-level physically-grounded augmentations.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Aug-nerf: Training stronger neural radiance fields with triple-level physically-grounded augmentations

Reference 6

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Observation ae82b787-fe0b-4a30-9464-25fc19aa07f4 · outbound

This paper cites Domain adaptation for underwater image enhancement via content and style sepa- ration.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Domain adaptation for underwater image enhancement via content and style sepa- ration

Reference 7

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 65f85693-eb4e-4bf3-be18-68fa5755a929 · outbound

This paper cites Depth-supervised nerf: Fewer views and faster train- ing for free.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Depth-supervised nerf: Fewer views and faster train- ing for free

Reference 8

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Observation ad41e53e-5806-47dd-bad5-ff0a89629c2d · outbound

This paper cites Aquagan: Restoration of underwater images.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Aquagan: Restoration of underwater images

Reference 9

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 13332a75-ba3c-4fc0-84e3-2f2d1e2c846d · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

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Observation 85d38c57-1716-490e-a9d1-7fa27a2fd88b · outbound

This paper cites Uncertainty inspired underwater image enhance- ment.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Uncertainty inspired underwater image enhance- ment

Reference 11

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Observation 1d8479f8-9761-416f-b786-543b8d1cd6ed · outbound

This paper cites Uncertainty inspired underwater image enhance- ment.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Uncertainty inspired underwater image enhance- ment

Reference 12

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Observation 4a6aba87-dd88-42fb-9de5-fd78b4aabadb · outbound

This paper cites Deep residual learning for image recognition.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Deep residual learning for image recognition

Reference 13

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Observation f6a70608-c6e0-4aab-8034-c603bf58c328 · outbound

This paper cites Ray tracing volume densities.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Ray tracing volume densities

Reference 14

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Observation f0854b82-52be-47b5-b822-623b4a4aac10 · outbound

This paper cites Zero-shot single image restoration through controlled perturbation of koschmieder’s model.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Zero-shot single image restoration through controlled perturbation of koschmieder’s model

Reference 15

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Observation d57153e2-6c8c-4de9-9fde-4975d99cadf0 · outbound

This paper cites Vision transformer for nerf-based view synthesis from a single input image.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Vision transformer for nerf-based view synthesis from a single input image

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 80067a8a-305b-4b18-8707-ce4376ec2404 · outbound

This paper cites Optical models for direct volume rendering.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Optical models for direct volume rendering

Reference 17

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Observation 58de959b-e197-4cc7-88e9-4788fb76446d · outbound

This paper cites Local light field fusion: Practical view syn- thesis with prescriptive sampling guidelines.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Local light field fusion: Practical view syn- thesis with prescriptive sampling guidelines

Reference 18

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Observation 4f76c9a9-8b73-45b4-b61b-3731c2480108 · outbound

This paper cites Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar

Reference 19

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Observation cb1ed9ea-9781-4566-b24c-5b0feb8dfb4b · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 20

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Observation ade068b8-903a-4b1c-ace0-b00f60a20fa5 · outbound

This paper cites Reg- nerf: Regularizing neural radiance fields for view synthesis from sparse inputs.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Reg- nerf: Regularizing neural radiance fields for view synthesis from sparse inputs

Reference 21

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Observation 3e0d9ac1-c114-4bcf-b526-9e85aa7ab13b · outbound

This paper cites Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction

Reference 22

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Observation 565614d7-7a2a-4b52-b2f8-5b4c4a576f32 · outbound

This paper cites Human-visual- system-inspired underwater image quality measures.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Human-visual- system-inspired underwater image quality measures

Reference 23

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Observation 17129125-ea97-4677-acf4-b6ffc5b0b78c · outbound

This paper cites Barron, Sofien Bouaziz, Dan B Goldman, Steven M.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Barron, Sofien Bouaziz, Dan B Goldman, Steven M

Reference 24

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Observation 14701c62-8f63-45a2-96ed-6b23bdf8f62e · outbound

This paper cites Hypernerf: A higher- dimensional representation for topologically varying neu- ral radiance fields.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Hypernerf: A higher- dimensional representation for topologically varying neu- ral radiance fields

Reference 25

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Observation d36d2640-27e6-4fdd-a396-ee4d4f4ee5cf · outbound

This paper cites Nan: Noise- aware nerfs for burst-denoising.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Nan: Noise- aware nerfs for burst-denoising

Reference 26

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Observation 59552b70-0f71-4c47-9960-b40c1538e58b · outbound

This paper cites D-nerf: Neural radiance fields for dynamic scenes.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields D-nerf: Neural radiance fields for dynamic scenes

Reference 27

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Observation 63319ba2-de82-4a88-92da-976b3b08b472 · outbound

This paper cites Structure-from-motion revisited.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Structure-from-motion revisited

Reference 28

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Observation 3bd8a50d-849f-46b1-82df-21408c464082 · outbound

This paper cites Pixelwise view selection for un- structured multi-view stereo.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Pixelwise view selection for un- structured multi-view stereo

Reference 29

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Observation 8a0b2cf9-ebe0-4f6c-a578-ecbff86266fb · outbound

This paper cites Is Attention All That NeRF Needs?.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Is Attention All That NeRF Needs?

Reference 30

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Observation ae5aa39f-496a-4eca-b6e8-f50e27d9623f · outbound

This paper cites Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields

Reference 31

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Observation 144645c3-d8e4-43bc-91b4-5087eddcf507 · outbound

This paper cites NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

Reference 32

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Observation 3982105f-975a-41d7-811b-55de70b2ebbd · outbound

This paper cites Ibr- net: Learning multi-view image-based rendering.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Ibr- net: Learning multi-view image-based rendering

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation aaa9b756-fe46-4a3e-8448-5d395744f5ba · outbound

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

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Image quality assessment: from error visibility to structural similarity

Reference 34

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Observation c980b793-37f3-4d6a-9b32-362c91d10a2e · outbound

This paper cites Nex: Real-time view synthesis with neural basis expansion.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Nex: Real-time view synthesis with neural basis expansion

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation af0a54b9-ab4c-4c35-a3e5-30264af2c5a6 · outbound

This paper cites SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image

Reference 36

Resolution
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no resolver link, observed 2026-08-12T13:32:57.740261Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:32:57.740261Z digest=sha256:cc4ebf853e47cd3e87a77e74c10f0cfd905c3e96661d3aaac319bbd159443b03

Observation 84b61d9f-9fd5-4650-a6aa-f4b394991292 · outbound

This paper cites An underwater color im- age quality evaluation metric.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields An underwater color im- age quality evaluation metric

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:32:57.861370Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:32:57.743609Z digest=sha256:c8da3c3af083de1fdc5f37c9cf3efc3c26bda2af97bfb61243162acdd1084e7f

Observation ca62b2e7-d290-467b-8b1e-1432eee30f4f · outbound

This paper cites V ol- ume rendering of neural implicit surfaces.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields V ol- ume rendering of neural implicit surfaces

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:32:57.851596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:32:57.746551Z digest=sha256:c4a6d058d403cfb7da3ad5277004efc57a5a1078aafddfc626a830c5d5bd5b76

Observation b931fc3a-cb02-4734-9bfc-980290e7df33 · outbound

This paper cites Fmsnet: Un- derwater image restoration by learning from a synthesized dataset.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Fmsnet: Un- derwater image restoration by learning from a synthesized dataset

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:32:57.842151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:32:57.749459Z digest=sha256:da66168c01f09916688a97e274a7d5d4059eabbad657df81074605a96cb2b66b

Observation 7b883658-a3be-43db-a455-3510554ccda7 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields The unreasonable effectiveness of deep features as a perceptual metric

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T13:32:57.752259Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:32:57.752259Z digest=sha256:6b10dd08eab06e6b8b2bba2d9c9809cdd8bd1775e57c0808ac3084669c092f6b

Observation 55c80654-9e07-43d0-9689-c6fbe33e6e14 · outbound

This paper cites A fast single image haze removal algorithm using color attenuation prior.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields A fast single image haze removal algorithm using color attenuation prior

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T13:32:57.755086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:32:57.755086Z digest=sha256:381cff6980d685bfcdfa2975fc65a82617869dccfc28998234e60e3610086e44

Observation 396d49ec-c8ec-45eb-b074-0c15e67148a3 · outbound

This paper cites Loss Functions To achieve the rendering and restoration in an unsuper- vised manner, it is important to regularise the model with appropriate losses.

U2NeRF: Unsupervised Underwater Image Restoration and Neural Radiance Fields Loss Functions To achieve the rendering and restoration in an unsuper- vised manner, it is important to regularise the model with appropriate losses

Reference 2015

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:32:57.820345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:32:57.758092Z digest=sha256:a068cc8bdddcad46e51863cf2a89b27c19054f041a218995a9556580134aaa98

Pith citing papers

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