Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution | Paper Citation Record

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This paper cites A new generative adversarial network for medical images super resolution.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution A new generative adversarial network for medical images super resolution

Reference 1

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This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 2

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This paper cites Strong trends in the skill of the era-40 and ncep–ncar reanalyses in the high and midlatitudes of the southern hemisphere, 1958–2001.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Strong trends in the skill of the era-40 and ncep–ncar reanalyses in the high and midlatitudes of the southern hemisphere, 1958–2001

Reference 3

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This paper cites A super-resolution diffusion model for recovering bone microstructure from ct images.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution A super-resolution diffusion model for recovering bone microstructure from ct images

Reference 4

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Observation f4d558ea-99a6-4c4d-abe7-d6e6501dde54 · outbound

This paper cites Deep generative image models using a laplacian pyramid of adversarial networks.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Deep generative image models using a laplacian pyramid of adversarial networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:27.213948Z

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Observation f41a1bb6-eb5b-4135-9901-f4c8f81ef2c1 · outbound

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

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Image super-resolution using deep convolutional networks

Reference 6

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This paper cites Overview and meteorological validation of the wind integration national dataset toolkit.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Overview and meteorological validation of the wind integration national dataset toolkit

Reference 7

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Observation dfa67c14-ed05-4643-a533-d252f2d10dcd · outbound

This paper cites Smith, Michael P.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Smith, Michael P

Reference 8

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Observation ba8c45db-f244-46cc-9984-d2356def2d4f · outbound

This paper cites Spectral neural operators.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Spectral neural operators

Reference 9

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This paper cites Pot: Python optimal transport.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Pot: Python optimal transport

Reference 10

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Observation 8b930de2-d851-4089-8ec4-4fe6e2d77511 · outbound

This paper cites A physics-informed variational deep- onet for predicting crack path in quasi-brittle materials.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution A physics-informed variational deep- onet for predicting crack path in quasi-brittle materials

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:27.152959Z

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Observation e71f3e39-9a7f-4e94-bff4-9d2cdac108c1 · outbound

This paper cites Climalign: Unsupervised statistical downscaling of cli- mate variables via normalizing flows.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Climalign: Unsupervised statistical downscaling of cli- mate variables via normalizing flows

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:27.142814Z

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Observation ee5e4e4c-2561-406b-952c-6a302577c6aa · outbound

This paper cites Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

Reference 13

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This paper cites Gnot: A general neural operator transformer for operator learning.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Gnot: A general neural operator transformer for operator learning

Reference 14

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Observation c94c272b-275c-4bfa-b5b9-3502420e827a · outbound

This paper cites Hard-constrained deep learning for climate downscaling.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Hard-constrained deep learning for climate downscaling

Reference 15

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Observation b14cefa8-b5b5-4a67-8c45-9f89731a0663 · outbound

This paper cites A generative deep learning approach to stochastic downscaling of precipitation forecasts.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution A generative deep learning approach to stochastic downscaling of precipitation forecasts

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:27.115866Z

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Observation 57e6552d-7b85-4d77-9bff-c8ec4a23420b · outbound

This paper cites The era5 global reanalysis.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution The era5 global reanalysis

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:27.105840Z

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Observation 03583682-8753-49ba-9153-f10bb358aa90 · outbound

This paper cites Denoising diffusion probabilistic models.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Denoising diffusion probabilistic models

Reference 18

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Observation 744cde46-33cc-4ea6-b9ed-03f8abedf21d · outbound

This paper cites The ncep/ncar 40-year reanalysis project.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution The ncep/ncar 40-year reanalysis project

Reference 19

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Observation 9fa25f5f-0cee-46d0-99cb-a4620cd9fd1d · outbound

This paper cites Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge

Reference 20

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Observation 9d731b0b-bc12-4833-aa06-66125d0dbf4e · outbound

This paper cites Ma- chine learning–accelerated computational fluid dynamics.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Ma- chine learning–accelerated computational fluid dynamics

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:27.080051Z

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Observation 1e71e6de-36dd-4303-b02d-8ee022bc182d · outbound

This paper cites Neural Optimal Transport.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Neural Optimal Transport

Reference 22

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Observation f4a517f5-c5c4-4c11-9f93-011d82faaa1c · outbound

This paper cites Adaptive estimation of a quadratic functional by model selection.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Adaptive estimation of a quadratic functional by model selection

Reference 23

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Observation 53f61f23-4cc0-4faf-a3bd-8c10da599f91 · outbound

This paper cites Patch complexity, finite pixel correlations and optimal denoising.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Patch complexity, finite pixel correlations and optimal denoising

Reference 24

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Observation 22d42749-002b-49b5-9bc2-bd5829cd1f13 · outbound

This paper cites Generative Adversarial Models for Extreme Geospatial Downscaling.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Generative Adversarial Models for Extreme Geospatial Downscaling

Reference 25

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This paper cites Srdiff: Single image super-resolution with diffusion probabilistic models.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Srdiff: Single image super-resolution with diffusion probabilistic models

Reference 26

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Observation 52915a86-0646-4dca-b3e0-72f3ace90e65 · outbound

This paper cites ADBM: Adversarial diffusion bridge model for reliable adversarial purification.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution ADBM: Adversarial diffusion bridge model for reliable adversarial purification

Reference 27

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Observation d201e594-f60a-4c33-8f94-a4b7280ad82b · outbound

This paper cites Transformer for Partial Differential Equations' Operator Learning.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Transformer for Partial Differential Equations' Operator Learning

Reference 28

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Observation dc49d12f-1f6d-4180-9e0d-e9bf3d85de53 · outbound

This paper cites Fourier neural operator for parametric partial differential equations.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Fourier neural operator for parametric partial differential equations

Reference 29

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Observation 535eed34-8e5b-46ab-bdfc-dd79c2f27a4c · outbound

This paper cites Physics-informed neural operator for learning partial differential equations.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Physics-informed neural operator for learning partial differential equations

Reference 30

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

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Enhanced deep residual networks for single image super-resolution

Reference 31

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This paper cites an unresolved cited work.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Unresolved cited work

Reference 32

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Observation 746b807b-36a0-4352-8e27-2393c66711da · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 33

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Observation 944a5780-0829-44f8-baa0-5801050e5d89 · outbound

This paper cites Learning nonlinear oper- ators via DeepONet based on the universal approximation theorem of operators.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Learning nonlinear oper- ators via DeepONet based on the universal approximation theorem of operators

Reference 34

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Observation 8b92d6cd-d223-469d-964a-bf3d9261429c · outbound

This paper cites Generative downscaling of pde solvers with physics-guided diffusion models.Journal of Scientific Computing, 101(71), 2024.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Generative downscaling of pde solvers with physics-guided diffusion models.Journal of Scientific Computing, 101(71), 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.996778Z

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Observation 322a2bf0-966c-4ed4-88bb-89d728d231c4 · outbound

This paper cites Evaluation of era-40, ncep-1, and ncep-2 reanalysis air temperatures with ground-based measurements in china.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Evaluation of era-40, ncep-1, and ncep-2 reanalysis air temperatures with ground-based measurements in china

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.986602Z

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Observation 1961ac9f-a84e-4037-87e4-4038a3f95cdf · outbound

This paper cites Image restoration using very deep convolutional encoder- decoder networks with symmetric skip connections.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Image restoration using very deep convolutional encoder- decoder networks with symmetric skip connections

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.975194Z

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Observation 4e373c40-4210-406f-9031-231944716a0f · outbound

This paper cites Multiple Physics Pretraining for Physical Surrogate Models.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Multiple Physics Pretraining for Physical Surrogate Models

Reference 38

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Observation c3e84712-854b-4624-9f65-d0af074213b4 · outbound

This paper cites Generation of random distribution of fibres in long-fibre reinforced composites.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Generation of random distribution of fibres in long-fibre reinforced composites

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.964155Z

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Observation 5dc119e2-a6b8-437d-b3a8-a9925fe708b0 · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 40

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Observation 080b9f37-2b12-4409-9247-069ad53dd151 · outbound

This paper cites A numerical method for computing the overall response of nonlinear compos- ites with complex microstructure.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution A numerical method for computing the overall response of nonlinear compos- ites with complex microstructure

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.954037Z

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Observation fab0b1df-d11f-4b9b-ab4c-51f10eb49875 · outbound

This paper cites Diffusion Models for Adversarial Purification.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Diffusion Models for Adversarial Purification

Reference 42

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Observation 8bb7dbe4-9944-47c3-9bd5-ee42b0a07206 · outbound

This paper cites A robust generative adversarial network approach for climate downscaling and weather generation.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution A robust generative adversarial network approach for climate downscaling and weather generation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.943682Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4c36e6df-be9e-4269-9954-3a0818f5ca27 · outbound

This paper cites Im- age super-resolution via iterative refinement.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Im- age super-resolution via iterative refinement

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.932801Z

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Observation b05faa66-53e9-4ea6-81b0-87b86687452d · outbound

This paper cites On the Effectiveness of Neural Operators at Zero-Shot Weather Downscaling.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution On the Effectiveness of Neural Operators at Zero-Shot Weather Downscaling

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:57:26.677153Z

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Observation 118fc7ae-c5ba-415d-aa00-0bc394ba56e6 · outbound

This paper cites Denoising Diffusion Implicit Models.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Denoising Diffusion Implicit Models

Reference 46

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Observation ab12c07e-eb04-4ec1-b58a-cdcc8c2b1d3c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Score-Based Generative Modeling through Stochastic Differential Equations

Reference 47

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Observation 9960d861-f47f-41a6-8b7b-c1eb8c52cd1f · outbound

This paper cites Dual Diffusion Implicit Bridges for Image-to-Image Translation.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Dual Diffusion Implicit Bridges for Image-to-Image Translation

Reference 48

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Observation d8dfb0a0-8d2b-41da-8cac-98719f1e5a0d · outbound

This paper cites Debias coarsely, sample conditionally: Statistical downscaling through optimal transport and probabilis- tic diffusion models.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Debias coarsely, sample conditionally: Statistical downscaling through optimal transport and probabilis- tic diffusion models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.922847Z

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Observation d29a162f-50ba-456e-a16c-ed207c6d0726 · outbound

This paper cites Guided Diffusion Model for Adversarial Purification.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Guided Diffusion Model for Adversarial Purification

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:26.454107Z

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Observation 8820123e-14a6-4726-986b-c8753debb1b2 · outbound

This paper cites Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:26.457934Z

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Observation 46920b61-dcca-44ce-bd6b-bc9e14bf11b0 · outbound

This paper cites Long-time integration of parametric evolution equations with physics-informed deeponets.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Long-time integration of parametric evolution equations with physics-informed deeponets

Reference 52

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Observation dfcad5f4-9b13-4519-bf6e-489469c7f8ae · outbound

This paper cites CViT: Continuous Vision Transformer for Operator Learning.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution CViT: Continuous Vision Transformer for Operator Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:26.464165Z

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Observation e2d61151-c43b-467c-958e-618d7dab639a · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed deeponets.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Learning the solution operator of parametric partial differential equations with physics-informed deeponets

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:26.467644Z

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This paper cites Improved architectures and training algorithms for deep operator networks.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Improved architectures and training algorithms for deep operator networks

Reference 55

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Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Generative Diffusion-based Downscaling for Climate

Reference 56

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Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Super-resolution neural operator

Reference 57

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This paper cites U-fno—an en- hanced fourier neural operator-based deep-learning model for multiphase flow.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution U-fno—an en- hanced fourier neural operator-based deep-learning model for multiphase flow

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.889351Z

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Observation 675c467c-c526-430c-b08a-2dd95df5b4cd · outbound

This paper cites Climate Variable Downscaling with Conditional Normalizing Flows.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Climate Variable Downscaling with Conditional Normalizing Flows

Reference 59

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Observation 85876b15-25bd-4765-87bb-26756905674d · outbound

This paper cites Diffusion-based Models for Unpaired Super-resolution in Fluid Dynamics.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Diffusion-based Models for Unpaired Super-resolution in Fluid Dynamics

Reference 60

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This paper cites Diffusion models: A comprehensive survey of methods and applications.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Diffusion models: A comprehensive survey of methods and applications

Reference 61

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Observation ca5ea8f6-82d0-48c2-84a2-424dbecc6928 · outbound

This paper cites Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Reference 62

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Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Unresolved cited work

Reference 63

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This paper cites Latent diffusion model-based mri superresolution enhances mild cognitive impairment prognostication and alzheimer’s disease classification.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Latent diffusion model-based mri superresolution enhances mild cognitive impairment prognostication and alzheimer’s disease classification

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.862551Z

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Observation f2d5e28a-a611-4869-8640-4dd3391c4639 · outbound

This paper cites Adversarial purification with score-based generative models.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Adversarial purification with score-based generative models

Reference 65

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Observation e0dee7cd-f86d-4a45-bf57-a9ef0489a3f6 · outbound

This paper cites Learning deep implicit fourier neural operators (ifnos) with applications to heterogeneous material modeling.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Learning deep implicit fourier neural operators (ifnos) with applications to heterogeneous material modeling

Reference 66

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Observation c90263df-c2e5-4bb1-b120-14578e6eafda · outbound

This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:26.510750Z

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Observation 1bc458d0-3731-48b6-b2d8-80d151398293 · outbound

This paper cites Ffdnet: Toward a fast and flexible solution for cnn-based image denoising.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Ffdnet: Toward a fast and flexible solution for cnn-based image denoising

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.833222Z

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Observation 690a385b-fa17-450f-9e31-0c25906d19ec · outbound

This paper cites Fast Sampling of Diffusion Models with Exponential Integrator.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Fast Sampling of Diffusion Models with Exponential Integrator

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:26.517451Z

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Observation cc6951b1-d84e-4a5e-a7e5-41f61c024933 · outbound

This paper cites Unpaired image-to-image translation using cycle- consistent adversarial networks.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Unpaired image-to-image translation using cycle- consistent adversarial networks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.822827Z

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Observation 6a118eee-f975-442b-a2ae-a1c0f422275e · outbound

This paper cites Fourier-deeponet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Fourier-deeponet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.812059Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 504726ac-81c0-437f-95f4-5341b51a960a · outbound

This paper cites ˆuh(t∗ 1,t∗.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution ˆuh(t∗ 1,t∗

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:26.800437Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ff25058-9577-4a89-b17c-9c457388bdf1 · outbound

This paper cites an unresolved cited work.

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Unresolved cited work

Reference 73

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