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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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