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Paper Citation Record · LEDGER

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models

As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2412.00156.

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

pith.paper-citation-record.v1
2412.00156 v4

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:10:10.375561Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

38 of 38 outbound references displayed

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  • verified fuzzy22
  • unresolved15
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b3e4f921-afae-4c52-827e-4d0c0bb65ee7 · outbound

This paper cites Improving diffusion models for inverse prob- lems using manifold constraints.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Improving diffusion models for inverse prob- lems using manifold constraints

Reference 1

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Observation 2ce72512-8b57-4e1a-a1fa-793a4fcea469 · outbound

This paper cites Diffusion pos- terior sampling for general noisy inverse problems.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Diffusion pos- terior sampling for general noisy inverse problems

Reference 2

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Observation c99a6b8b-7c7c-4da8-b8d0-c628d2614972 · outbound

This paper cites Prompt-tuning latent diffusion models for inverse problems.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Prompt-tuning latent diffusion models for inverse problems

Reference 3

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Unavailable: canonical work link unavailable.

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Observation 8ec58245-bf43-4188-9452-ee69cf8d2cd7 · outbound

This paper cites De- composed diffusion sampler for accelerating large-scale in- verse problems.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models De- composed diffusion sampler for accelerating large-scale in- verse problems

Reference 4

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Source-reported events for the cited work

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Observation 4c221c04-a267-4a70-bb92-0aa0998878ea · outbound

This paper cites Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models

Reference 5

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Observation 891a8839-38a7-4927-9bb1-3c9828d87188 · outbound

This paper cites Diffusion models beat gans on image synthesis.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Diffusion models beat gans on image synthesis

Reference 6

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Observation 0e26dc5e-2ce9-4686-a355-afb526601252 · outbound

This paper cites Tweedie’s formula and selection bias.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Tweedie’s formula and selection bias

Reference 7

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Observation e140eaa9-d8dd-452a-a301-2116c70c3dab · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 8

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Observation 75f86fcc-f4df-4901-b47d-25e5bdea7897 · outbound

This paper cites Denoising diffu- sion probabilistic models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Denoising diffu- sion probabilistic models

Reference 9

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Source-reported events for the cited work

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Observation f503e66c-8999-43a3-a85d-bb39e0b5f1fa · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Elucidating the design space of diffusion-based generative models

Reference 10

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Observation 805d62b0-ea61-4903-8f04-2dbac8c39bd6 · outbound

This paper cites Denoising diffusion restoration models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Denoising diffusion restoration models

Reference 11

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Observation 5163ef75-c089-453f-969a-2dae00b0bbc5 · outbound

This paper cites Regularization by Texts for Latent Diffusion Inverse Solvers.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Regularization by Texts for Latent Diffusion Inverse Solvers

Reference 12

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Observation ec1a3f41-50ac-4945-959e-b70a7a1cb1cd · outbound

This paper cites Solving video inverse problems using image diffusion models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Solving video inverse problems using image diffusion models

Reference 13

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Source-reported events for the cited work

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Observation a631c117-8541-4eba-992b-da8510037f4a · outbound

This paper cites Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion

Reference 14

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Observation 7757f6cd-b173-4347-bb39-17cfcc2a1a52 · outbound

This paper cites Deep multi-scale convolutional neural network for dynamic scene deblurring.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Deep multi-scale convolutional neural network for dynamic scene deblurring

Reference 15

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Observation 05d5ce81-c86b-4453-922e-cfcba60867cc · outbound

This paper cites Improved denoising diffusion probabilistic models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Improved denoising diffusion probabilistic models

Reference 16

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Observation 26c776ee-c032-4a64-b08a-b68f0e49f230 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 17

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Observation 1c5f473d-7d9c-421b-8410-a6c439e9ba35 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models The 2017 DAVIS Challenge on Video Object Segmentation

Reference 18

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Observation ced56341-e002-431e-88cb-e6f5cbf0f441 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 19

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Observation a1573282-24bd-407b-8c36-f1b0c013885c · outbound

This paper cites Solving linear inverse problems provably via posterior sampling with latent diffusion models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Solving linear inverse problems provably via posterior sampling with latent diffusion models

Reference 20

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Source-reported events for the cited work

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Observation c2fb1cb4-c7d9-4a5e-b4af-ba499c4ccc29 · outbound

This paper cites Denois- ing diffusion implicit models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Denois- ing diffusion implicit models

Reference 21

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Source-reported events for the cited work

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Observation 1f4b358d-73a0-4832-a282-739dc8f00066 · outbound

This paper cites Pseudoinverse-guided diffusion models for inverse problems.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Pseudoinverse-guided diffusion models for inverse problems

Reference 22

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Observation 8f039b4a-6d79-4234-8c0c-b4eac8f878a8 · outbound

This paper cites Score-based generative modeling through stochastic differential equa- tions.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Score-based generative modeling through stochastic differential equa- tions

Reference 23

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Observation 3ad840d5-3f73-4cf9-be09-5ee38d2fb766 · outbound

This paper cites Evaluating a synthetic image dataset gener- ated with stable diffusion.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Evaluating a synthetic image dataset gener- ated with stable diffusion

Reference 24

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Observation 5e48e0af-09ae-4564-8478-07e69817a35b · outbound

This paper cites High-resolution image re- construction with latent diffusion models from human brain activity.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models High-resolution image re- construction with latent diffusion models from human brain activity

Reference 25

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Observation 9c1befb1-9331-46c4-b2e6-a1ad7354e5cf · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Raft: Recurrent all-pairs field transforms for optical flow

Reference 26

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Source-reported events for the cited work

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Observation 1e800a37-ab2c-42c0-80c2-3bd3fd9157cb · outbound

This paper cites Fvd: A new metric for video generation.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Fvd: A new metric for video generation

Reference 27

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Source-reported events for the cited work

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Observation 34ef87cb-38d9-4d99-929d-d6d0a7201e07 · outbound

This paper cites Zero-shot image restoration using denoising diffusion null-space model.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Zero-shot image restoration using denoising diffusion null-space model

Reference 28

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Source-reported events for the cited work

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Observation 2c3f7511-4765-41ea-9bb7-92fcfd416c5e · outbound

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

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Image quality assessment: from error visibility to structural similarity

Reference 29

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Observation b8272ef0-ffc0-4ad1-8b24-bce944eeaeef · outbound

This paper cites Dreamclean: Restoring clean image using deep diffusion prior.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Dreamclean: Restoring clean image using deep diffusion prior

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 617bc5ba-cb68-4f80-a519-26a1a9ba37a3 · outbound

This paper cites Gmflow: Learning optical flow via global matching.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Gmflow: Learning optical flow via global matching

Reference 31

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Observation 5f9956ca-68e3-4141-ba87-32ba18b3ec6b · outbound

This paper cites Diffusion probabilistic model made slim.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Diffusion probabilistic model made slim

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 1b75589d-5362-4937-aa71-8bd3196ca23e · outbound

This paper cites Diffir2vr- zero: Zero-shot video restoration with diffusion-based image restoration models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Diffir2vr- zero: Zero-shot video restoration with diffusion-based image restoration models

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 266df45c-f81d-460d-8e26-b6fe7e43883d · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Adding conditional control to text-to-image diffusion models

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 830bfb94-39ff-4aee-b767-add2656d41d8 · outbound

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

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models The unreasonable effectiveness of deep features as a perceptual metric

Reference 35

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T10:10:10.361009Z digest=sha256:b8b19800f42f3f5d715e7740f9db35fe724b4ed56dc23fb317d74870d9210334

Observation a134eabb-1913-4cc2-9438-744b1617ed71 · outbound

This paper cites Implementation of Comparative Methods SVI [13].

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Implementation of Comparative Methods SVI [13]

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:10:10.683115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T10:10:10.366180Z digest=sha256:ab816d61270680ad4c98d038d60beed363e4204cac0edae25915cb52e2d2642b

Observation 51d6d8d6-6117-48bd-8efc-c3b3d6938616 · outbound

This paper cites Here, we provide an example application of our method to blind video de- blurring, showing its potential as a general framework for solving blind video inverse problems.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models Here, we provide an example application of our method to blind video de- blurring, showing its potential as a general framework for solving blind video inverse problems

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:10:10.664985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T10:10:10.370545Z digest=sha256:ce568167cb5fc33006d31376016edffe660b534e1fee29ffb1568b18371ea2c7

Observation 7476524a-fc54-4e57-881e-45c62b3c3f20 · outbound

This paper cites The page features 36 paired visualizations of mea- surements and reconstructions across various aspect ratios and degradation types.

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models The page features 36 paired visualizations of mea- surements and reconstructions across various aspect ratios and degradation types

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T10:10:10.648596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T10:10:10.375561Z digest=sha256:28a0e25fb12b14a055971ab2e87bb1f8ad3437c8143abcec94a3bd2f5c36be25

Pith citing papers

No inbound Pith citation observations are available.