Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models | Paper Citation Record

Observation fb683419-7c7c-4b61-9c1a-a095af1a9d05 · outbound

This paper cites Deep Learning for Image/Video Restoration and Super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Deep Learning for Image/Video Restoration and Super-resolution

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.284762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 16d349f2-f553-455d-b369-50e8408f8066 · outbound

This paper cites Image Super-Resolution Using Deep Convolutional Networks.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Image Super-Resolution Using Deep Convolutional Networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.269706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c841d4cb-8ec2-4a53-97fe-87fba866f527 · outbound

This paper cites Photo-Realistic Single Image Super- Resolution Using a Generative Adversarial Network.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Photo-Realistic Single Image Super- Resolution Using a Generative Adversarial Network

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.254656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0a0367f-427f-42e0-ae78-d5ad007a06b4 · outbound

This paper cites Enhanced Deep Residual Networks for Single Image Super-Resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Enhanced Deep Residual Networks for Single Image Super-Resolution

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.239798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a12f283-f113-4699-9218-1dfcdb10ae76 · outbound

This paper cites Image Super-Resolution Using Very Deep Residual Channel Attention Networks.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.225214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 98a485cb-67bf-4702-a12a-59b5b1d70796 · outbound

This paper cites ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.209751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6a340f0b-afb7-49ea-8f5e-64b17ddc78a3 · outbound

This paper cites ESRGAN+: Fur- ther improving enhanced super-resolution generative ad- versarial network.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models ESRGAN+: Fur- ther improving enhanced super-resolution generative ad- versarial network

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.194727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c48aa73-182a-46ad-8a52-8102bf48ef19 · outbound

This paper cites Perception-Oriented Single Image Super-Resolution Using Optimal Objec- tive Estimation.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Perception-Oriented Single Image Super-Resolution Using Optimal Objec- tive Estimation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.177792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8df103b0-55e6-43fc-a400-9ffc48f9e7af · outbound

This paper cites Pixel-Aware Stable Diffusion for Real- istic Image Super-Resolution and Personalized Styliza- tion.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Pixel-Aware Stable Diffusion for Real- istic Image Super-Resolution and Personalized Styliza- tion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.161871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 07d90b7e-4375-49c1-9d19-2e198614ef73 · outbound

This paper cites Structure-Preserving Super Resolution with Gradient Guidance.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Structure-Preserving Super Resolution with Gradient Guidance

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.146259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7545a7da-ce3a-47ee-8844-4ceb53a0839e · outbound

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

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.130855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 20111dbb-d7c1-4b0c-801b-d65f9cb7eba9 · outbound

This paper cites Activating More Pixels in Image Super- Resolution Transformer.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Activating More Pixels in Image Super- Resolution Transformer

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.115332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c4138ada-04dc-4dd2-b705-c59451d0facd · outbound

This paper cites Details or artifacts: A locally discriminative learning approach to realistic image super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Details or artifacts: A locally discriminative learning approach to realistic image super-resolution

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.098991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50a2fa70-8cde-42ea-8432-0f2e0d737fca · outbound

This paper cites Trustworthy SR: Resolving ambiguity in image super-resolution via diffusion models and human feedback.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Trustworthy SR: Resolving ambiguity in image super-resolution via diffusion models and human feedback

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.082338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f3abb73a-0e5d-48b2-9bc7-85fa10f08d7b · outbound

This paper cites Single image super- resolution from transformed self-exemplars.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Single image super- resolution from transformed self-exemplars

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.067141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2885ad0d-944f-4b62-aa86-01c07dee48ec · outbound

This paper cites SRFlow-DA: Super- Resolution Using Normalizing Flow with Deep Convo- lutional Block.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models SRFlow-DA: Super- Resolution Using Normalizing Flow with Deep Convo- lutional Block

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.050763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce3a1a5d-af9f-4d00-820e-994c7a165811 · outbound

This paper cites Image super-resolution via iterative refinement.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Image super-resolution via iterative refinement

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.034625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f51da559-04e0-4859-b1d3-a4c1252fb028 · outbound

This paper cites Perception-Distortion Trade-Off in the SR Space Spanned by Flow Models.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Perception-Distortion Trade-Off in the SR Space Spanned by Flow Models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.018818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd47420d-4374-4d06-9744-1771eca04b14 · outbound

This paper cites Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:45:12.491368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb9add40-f71c-40d5-832e-089fb22035dc · outbound

This paper cites Improving diffusion models for inverse problems using manifold constraints.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Improving diffusion models for inverse problems using manifold constraints

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:13.002601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c824e40a-9e89-4d36-9e35-b3d2b3d7a027 · outbound

This paper cites Learning continuous image representation with local implicit image func- tion.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Learning continuous image representation with local implicit image func- tion

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.986520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d589469b-a513-4be4-a6d0-ea9f48bef1ec · outbound

This paper cites One-Step Effective Diffusion Network for Real-World Image Super-Resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models One-Step Effective Diffusion Network for Real-World Image Super-Resolution

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:11.319127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e30963a9-0571-4a79-9fb5-5392799cdf1b · outbound

This paper cites SeeSR: Towards semantics-aware real-world image super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models SeeSR: Towards semantics-aware real-world image super-resolution

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.970122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c4f2bb2-86d3-43ee-acec-60096aad2fb8 · outbound

This paper cites Exploiting diffusion prior for real- world image super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Exploiting diffusion prior for real- world image super-resolution

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.951997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a828b17d-1cc4-4823-b892-fa299d095bcc · outbound

This paper cites The Unreasonable Effectiveness of Deep Features as a Perceptual Metric.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.934890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d6e7546-f44f-4428-be62-5aa55140fe25 · outbound

This paper cites Image Quality Assessment: Unifying Structure and Texture Similarity.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Image Quality Assessment: Unifying Structure and Texture Similarity

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.918409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f320c928-1667-4501-beee-7cdcfcfb0a42 · outbound

This paper cites GANs trained by a two time-scale update rule converge to a local nash equilibrium.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models GANs trained by a two time-scale update rule converge to a local nash equilibrium

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.901142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db4eb9ef-fa37-496c-8c5d-55ba72930e33 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large lan- guage models.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large lan- guage models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.884168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation faca380a-782b-4839-9fa4-325f4b0b0a7e · outbound

This paper cites GPT-4 Technical Report.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models GPT-4 Technical Report

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:11.971994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 154a130f-c846-407b-8515-617888f071ab · outbound

This paper cites Residual dense network for image super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Residual dense network for image super-resolution

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.867195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 719e9458-06a1-480a-9f21-74598c1e7665 · outbound

This paper cites SwinIR: Image restoration using swin transformer.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models SwinIR: Image restoration using swin transformer

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.852065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe3660ab-f864-4a2b-91cd-2f5eea3461d7 · outbound

This paper cites Generative Adversarial Nets.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Generative Adversarial Nets

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.836695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 982d3889-dac9-4ebe-95f7-92cdd644dee5 · outbound

This paper cites Training generative image super-resolution models by wavelet- domain losses enables better control of artifacts.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Training generative image super-resolution models by wavelet- domain losses enables better control of artifacts

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.820568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75d3433c-c072-46b1-8a7c-074837f5e9d8 · outbound

This paper cites Variational autoen- coder for reference based image super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Variational autoen- coder for reference based image super-resolution

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.805074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8cf4959-164c-4de8-a1fd-ba232b4f44df · outbound

This paper cites FS-NCSR: Increasing diversity of the super-resolution space via frequency separation and noise-conditioned normalizing flow.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models FS-NCSR: Increasing diversity of the super-resolution space via frequency separation and noise-conditioned normalizing flow

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.789404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a611c84-29cb-457f-84a4-8d9706a751b7 · outbound

This paper cites Generative Pretraining From Pixels.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Generative Pretraining From Pixels

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.772609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a4007b2-4ab6-47f3-a3df-47ed31d85666 · outbound

This paper cites SinSR: diffusion-based image super- resolution in a single step.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models SinSR: diffusion-based image super- resolution in a single step

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.757674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bcd0a5b5-e50c-43f2-90e7-c4f7583987ae · outbound

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

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models SRDiff: Single image super-resolution with diffusion probabilistic models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.742481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5669ec0f-6a85-4857-be71-71b210a1c6c1 · outbound

This paper cites Pseudoinverse-Guided Diffusion Mod- els for Inverse Problems.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Pseudoinverse-Guided Diffusion Mod- els for Inverse Problems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.727488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ccc7b83-0cfa-45c2-8d55-2fca21db408c · outbound

This paper cites BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.712431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8dcf384d-a5e5-4865-bcc1-7afc2378c31c · outbound

This paper cites IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:45:12.437614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a65a87c6-4545-4e27-ab7f-20ee74abca30 · outbound

This paper cites Vision-language models for vision tasks: A survey.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Vision-language models for vision tasks: A survey

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.696303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7dec906-f5ca-4e2e-92a6-9172643a28d9 · outbound

This paper cites Quality Assessment in the Era of Large Models: A Survey.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Quality Assessment in the Era of Large Models: A Survey

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:45:12.414407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f03c6352-0c97-47d8-ac23-9cfb3a5106b1 · outbound

This paper cites Assessing GPT-4 multimodal perfor- mance in radiological image analysis.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Assessing GPT-4 multimodal perfor- mance in radiological image analysis

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.681770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18cad1b8-98de-443c-aa1f-b925417efaa1 · outbound

This paper cites ChatGPT in healthcare: a taxonomy and systematic review.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models ChatGPT in healthcare: a taxonomy and systematic review

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.666033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d48d6fb-795f-4b48-b03d-a79a3f54f89f · outbound

This paper cites The 2018 PIRM challenge on perceptual image super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models The 2018 PIRM challenge on perceptual image super-resolution

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.649124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cde337a6-982d-4927-ae66-149a911f2a88 · outbound

This paper cites NTIRE 2021 Learning the Super- Resolution Space Challenge.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models NTIRE 2021 Learning the Super- Resolution Space Challenge

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:12.066298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a00437b7-e7d9-4a9f-8c28-9b67eb683db9 · outbound

This paper cites The MNIST database of handwritten digit images for machine learning research.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models The MNIST database of handwritten digit images for machine learning research

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.634113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f621a413-d5a5-42f8-a3c6-81b6c898cdf3 · outbound

This paper cites Hierarchical conditional flow: A unified framework for image super-resolution and image rescal- ing.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Hierarchical conditional flow: A unified framework for image super-resolution and image rescal- ing

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.618044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 191fa6c5-59d7-40a2-ae81-5fe7d0172c3a · outbound

This paper cites https://llamaocr.com/.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models https://llamaocr.com/

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.603210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fcae0c9c-56aa-4499-8fe0-ad78d31ef77b · outbound

This paper cites On Single Image Scale-Up Using Sparse-Representations.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models On Single Image Scale-Up Using Sparse-Representations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.588516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e4856b1f-3261-4527-984f-daf6d8aac7e8 · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmen- tation algorithms and measuring ecological statistics.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models A database of human segmented natural images and its application to evaluating segmen- tation algorithms and measuring ecological statistics

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.571931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 86e29860-0216-46b4-8835-bdc08bc216d5 · outbound

This paper cites NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.555679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7708a877-033a-4bfd-8bef-9099b224a288 · outbound

This paper cites Implicit diffusion models for continuous super-resolution.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Implicit diffusion models for continuous super-resolution

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.539797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e6dc613-f60e-40f5-b5e0-4f3679859cdd · outbound

This paper cites KADID- 10k: A Large-scale Artificially Distorted IQA Database.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models KADID- 10k: A Large-scale Artificially Distorted IQA Database

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.523042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79f8bc66-82e6-4069-bf3c-378f64e66280 · outbound

This paper cites Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:12.507553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 98b26950-7a55-4597-9069-360066bb8a98 · outbound

This paper cites A Threshold Selection Method from Gray-Level Histograms.

Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models A Threshold Selection Method from Gray-Level Histograms

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:12.115778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers