Paper Citation Record · LEDGER

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts

As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.04525.

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

pith.paper-citation-record.v1
2412.04525 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:37:01.064895Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:52:29.585714Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T19:52:29.862894Z

Reference resolution

27 of 27 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c6c56659-cf07-48a1-81ff-60af636e61c9 · outbound

This paper cites A comprehensive review of deep learning-based single image super-resolution,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts A comprehensive review of deep learning-based single image super-resolution,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.750843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 964859ee-e2f2-4d99-9d1f-22ce5bfd0b6a · outbound

This paper cites NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:00.913075Z

Source-reported events for the cited work

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Observation 7b04339e-b670-43d7-b17d-df072a889dbd · outbound

This paper cites Beyond nyquist: A comparative analysis of 3d deep learning models enhancing mri resolution,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Beyond nyquist: A comparative analysis of 3d deep learning models enhancing mri resolution,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.715585Z

Source-reported events for the cited work

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Observation ec052695-a26a-46aa-a62f-1879cf4500fd · outbound

This paper cites Generating high- resolution CT slices from two image series using deep-learning-based resolution enhancement methods,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Generating high- resolution CT slices from two image series using deep-learning-based resolution enhancement methods,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.688811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c6035af-91c5-45d1-bab1-be5d456493b3 · outbound

This paper cites From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:37:01.218915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d9fb3e40-5f16-49ce-9696-2a52306d11b9 · outbound

This paper cites Image super-resolution using deep convolutional networks,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Image super-resolution using deep convolutional networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:00.936880Z

Source-reported events for the cited work

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Observation 97e33c6c-3a1f-42f4-baa7-70e3d8ed8fb0 · outbound

This paper cites Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.647874Z

Source-reported events for the cited work

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Observation c5720205-a1ef-4803-9d1d-2799b1040035 · outbound

This paper cites Deep residual learning for image recognition,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Deep residual learning for image recognition,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:00.947974Z

Source-reported events for the cited work

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Observation efd3f27e-f707-42dc-9c46-0f55b7053860 · outbound

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

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Photo-realistic single image super-resolution using a generative adversarial network,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.612144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4787340a-c2d1-430d-912b-0e45c41a3ed8 · outbound

This paper cites Enhanced deep residual networks for single image super-resolution,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Enhanced deep residual networks for single image super-resolution,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.592450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c42247e-0863-4f47-b8b8-4293990e6aed · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:00.966275Z

Source-reported events for the cited work

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Observation 070a0880-e03c-4322-8230-3913708b75d0 · outbound

This paper cites Esrgan: Enhanced super-resolution generative adversar- ial networks,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Esrgan: Enhanced super-resolution generative adversar- ial networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.572205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4228e837-a2da-410f-8dd9-4ec8c92939ac · outbound

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

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts The relativistic discriminator: a key element missing from standard GAN

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:00.977361Z

Source-reported events for the cited work

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Observation f65c2d3c-2181-4a3d-b466-a9eb78544f51 · outbound

This paper cites 2.5D deep learning for CT image reconstruction using a multi-gpu implementation,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts 2.5D deep learning for CT image reconstruction using a multi-gpu implementation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.550051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eff8e852-8747-415d-9a03-3deac2e4c138 · outbound

This paper cites Enabling rapid X- ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Enabling rapid X- ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.523197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1c6e084e-1c84-47a1-a700-4d38d935feaa · outbound

This paper cites Simurgh: A framework for cad-driven deep learning based X-Ray CT reconstruction,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Simurgh: A framework for cad-driven deep learning based X-Ray CT reconstruction,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.494532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7498bb23-26cb-4d60-a77a-039b1024110b · outbound

This paper cites MBIR Training for a 2.5D DL network in X-ray CT.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts MBIR Training for a 2.5D DL network in X-ray CT

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:37:01.125765Z

Source-reported events for the cited work

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Observation b0449d0f-6f4e-4054-9bc3-308657573449 · outbound

This paper cites Multi-slice fusion for sparse-view and limited-angle 4D CT reconstruc- tion,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Multi-slice fusion for sparse-view and limited-angle 4D CT reconstruc- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.472542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9904e77f-3094-4972-aac8-2b53dbb2ab46 · outbound

This paper cites LoDoInd: Introducing a benchmark low-dose industrial CT dataset and enhancing denoising with 2.5D deep learning techniques,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts LoDoInd: Introducing a benchmark low-dose industrial CT dataset and enhancing denoising with 2.5D deep learning techniques,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.453950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4e11145-da65-406f-ba92-a74264112c9d · outbound

This paper cites Deep learning based workflow for accelerated industrial X-ray computed tomography,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Deep learning based workflow for accelerated industrial X-ray computed tomography,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.433880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9bb6cb63-d90b-4043-b597-c3423ed4a7fd · outbound

This paper cites Bridging 2D and 3D seg- mentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Bridging 2D and 3D seg- mentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.413222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 53b6ee11-ac14-48e9-ad5a-06e8e0f0bf4a · outbound

This paper cites Spekpy v2. 0—a software toolkit for modeling x-ray tube spectra,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Spekpy v2. 0—a software toolkit for modeling x-ray tube spectra,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.391394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3eba195a-e5b1-4752-bee4-e9a42bbe50bd · outbound

This paper cites A validation of spekpy: A software toolkit for modelling x-ray tube spectra,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts A validation of spekpy: A software toolkit for modelling x-ray tube spectra,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.370623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aaa7a504-b942-4933-9db6-4eed07ca2938 · outbound

This paper cites Practical cone-beam algorithm,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Practical cone-beam algorithm,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.351276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 856ef3a9-630b-40d7-827e-11f4a5f8eee4 · outbound

This paper cites Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.326334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f525c1d7-8d70-419a-9c9c-9f6448afadc9 · outbound

This paper cites Direct iterative reconstruction of multiple basis material images in photon-counting spectral CT,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Direct iterative reconstruction of multiple basis material images in photon-counting spectral CT,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.291276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1da9fd69-199f-4771-8abf-d576fdef8cc6 · outbound

This paper cites Neural Network-based Single-material Beam Hardening Correction for X-ray CT in Addi- tive Manufacturing ,.

2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts Neural Network-based Single-material Beam Hardening Correction for X-ray CT in Addi- tive Manufacturing ,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:37:01.267287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 21cf6ce3-bd66-4c5e-9b53-950f82f0cbfb · inbound

Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction cites this paper.

Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction 2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T19:52:29.869148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.