Augmenting Perceptual Super-Resolution via Image Quality Predictors | Paper Citation Record

Observation 4ed3caeb-b9e3-4768-a809-08c14b5c52dc · outbound

This paper cites ARNIQA: Learning distortion manifold for image quality assessment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors ARNIQA: Learning distortion manifold for image quality assessment

Reference 1

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Observation 64dd3236-cba8-42cd-a15b-cd12dccfc8a3 · outbound

This paper cites Fast, accurate, and lightweight super- resolution with cascading residual network.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Fast, accurate, and lightweight super- resolution with cascading residual network

Reference 2

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This paper cites Explorable super resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Explorable super resolution

Reference 3

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Observation a3ed7bcc-ef05-4b55-95d0-85da4c60fbf1 · outbound

This paper cites Limits on super- resolution and how to break them.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Limits on super- resolution and how to break them

Reference 4

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This paper cites The perception- distortion tradeoff.

Augmenting Perceptual Super-Resolution via Image Quality Predictors The perception- distortion tradeoff

Reference 5

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Observation 590b325f-dd35-4d5f-8a36-8d23013faab2 · outbound

This paper cites Rethinking lossy compression: The rate-distortion-perception trade- off.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Rethinking lossy compression: The rate-distortion-perception trade- off

Reference 6

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Observation ac0829ea-d79d-44e3-bc3b-4c747f3bd243 · outbound

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

Augmenting Perceptual Super-Resolution via Image Quality Predictors The 2018 PIRM challenge on perceptual image super-resolution

Reference 7

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This paper cites Deep neural networks for no-reference and full- reference image quality assessment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Deep neural networks for no-reference and full- reference image quality assessment

Reference 8

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Observation 8c769ba9-0597-4604-9750-b965bbf8d3f8 · outbound

This paper cites Toward real-world single im- age super-resolution: A new benchmark and a new model.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Toward real-world single im- age super-resolution: A new benchmark and a new model

Reference 9

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Observation 416e1c24-a402-48c7-a907-54c7578086d3 · outbound

This paper cites IQA-PyTorch: Py- torch toolbox for image quality assessment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors IQA-PyTorch: Py- torch toolbox for image quality assessment

Reference 10

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Observation be8aa1b9-8846-44c5-a862-15091b8f521b · outbound

This paper cites TOPIQ: A top-down approach from semantics to distortions for image quality assessment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors TOPIQ: A top-down approach from semantics to distortions for image quality assessment

Reference 11

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Observation 3e6a8c9d-9553-4757-99f6-48e64be4252b · outbound

This paper cites Human guided ground-truth generation for realistic image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Human guided ground-truth generation for realistic image super-resolution

Reference 12

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Observation aecd984b-9335-4f90-b106-27b04d3ef51b · outbound

This paper cites Simple baselines for image restoration.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Simple baselines for image restoration

Reference 13

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Observation 697de8a1-5c7d-4b84-b945-4035d42e9e02 · outbound

This paper cites Activating more pixels in image super-resolution transformer.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Activating more pixels in image super-resolution transformer

Reference 14

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Observation 3e0c1b30-b20b-47c8-981d-e7f46735e480 · outbound

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Augmenting Perceptual Super-Resolution via Image Quality Predictors Unresolved cited work

Reference 15

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Observation c42c03f6-4a5c-4573-b7be-cf152c137a54 · outbound

This paper cites Directly fine-tuning diffusion models on dif- ferentiable rewards.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Directly fine-tuning diffusion models on dif- ferentiable rewards

Reference 16

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Observation 1e4dbf2a-d84a-46bf-b183-f0e821424692 · outbound

This paper cites Inversion by direct iteration: An alternative to denoising diffu- sion for image restoration.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Inversion by direct iteration: An alternative to denoising diffu- sion for image restoration

Reference 17

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This paper cites Image quality assessment: Unifying struc- ture and texture similarity.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Image quality assessment: Unifying struc- ture and texture similarity

Reference 18

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Observation aefe3150-71a7-4c16-a92b-ad60dfb63bbc · outbound

This paper cites Comparison of full-reference image qual- ity models for optimization of image processing sys- tems.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Comparison of full-reference image qual- ity models for optimization of image processing sys- tems

Reference 19

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This paper cites Si- moncelli.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Si- moncelli

Reference 20

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Observation d941bd11-fc9c-49c6-8ef2-47b9d1c944e5 · outbound

This paper cites Learning a deep convolutional network for image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Learning a deep convolutional network for image super-resolution

Reference 21

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Observation 6893ac9c-6ea6-4bb2-9e08-861aac8bb092 · outbound

This paper cites Accelerating the super-resolution convolutional neu- ral network.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Accelerating the super-resolution convolutional neu- ral network

Reference 22

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This paper cites DPOK: reinforcement learning for fine- tuning text-to-image diffusion models.

Augmenting Perceptual Super-Resolution via Image Quality Predictors DPOK: reinforcement learning for fine- tuning text-to-image diffusion models

Reference 23

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Augmenting Perceptual Super-Resolution via Image Quality Predictors Example-based super-resolution

Reference 24

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Observation 3f25f1b4-1e2e-4853-a949-2ffa9fb6e478 · outbound

This paper cites DreamSim: Learning new dimensions of hu- man visual similarity using synthetic data.

Augmenting Perceptual Super-Resolution via Image Quality Predictors DreamSim: Learning new dimensions of hu- man visual similarity using synthetic data

Reference 25

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Observation 019383f0-30ba-4f69-8cfd-e80bdb1b8ead · outbound

This paper cites Parameter-Efficient Fine-Tuning of State Space Models.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Parameter-Efficient Fine-Tuning of State Space Models

Reference 26

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Observation c125640e-5b2c-4f4b-9924-4e2964a7b18a · outbound

This paper cites R-LPIPS: An Adversarially Robust Perceptual Similarity Metric.

Augmenting Perceptual Super-Resolution via Image Quality Predictors R-LPIPS: An Adversarially Robust Perceptual Similarity Metric

Reference 27

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Observation f7e328d4-e056-40a4-9cf2-4560caf37feb · outbound

This paper cites Shift-tolerant per- ceptual similarity metric.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Shift-tolerant per- ceptual similarity metric

Reference 28

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This paper cites No-reference image quality assessment via transformers, relative ranking, and self-consistency.

Augmenting Perceptual Super-Resolution via Image Quality Predictors No-reference image quality assessment via transformers, relative ranking, and self-consistency

Reference 29

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This paper cites Generative adver- sarial nets.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Generative adver- sarial nets

Reference 30

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Augmenting Perceptual Super-Resolution via Image Quality Predictors Explaining and harnessing adversarial ex- amples

Reference 31

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Observation e527cb0a-61ea-48b5-9884-e7cc5c022866 · outbound

This paper cites MambaIR: A sim- ple baseline for image restoration with state-space model.

Augmenting Perceptual Super-Resolution via Image Quality Predictors MambaIR: A sim- ple baseline for image restoration with state-space model

Reference 32

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This paper cites Guardians of image quality: Benchmarking defenses against adversarial attacks on image quality metrics.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Guardians of image quality: Benchmarking defenses against adversarial attacks on image quality metrics

Reference 33

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Observation ab8fcbb4-b8be-4a98-a2d3-96af9fe6efd5 · outbound

This paper cites GANs trained by a two time-scale update rule con- verge to a local Nash equilibrium.

Augmenting Perceptual Super-Resolution via Image Quality Predictors GANs trained by a two time-scale update rule con- verge to a local Nash equilibrium

Reference 34

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This paper cites KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment

Reference 35

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This paper cites LoRA: Low-rank adaptation of large language models.

Augmenting Perceptual Super-Resolution via Image Quality Predictors LoRA: Low-rank adaptation of large language models

Reference 36

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Augmenting Perceptual Super-Resolution via Image Quality Predictors Real-world super- resolution via kernel estimation and noise injection

Reference 37

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This paper cites Tackling the ill-posedness of super- resolution through adaptive target generation.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Tackling the ill-posedness of super- resolution through adaptive target generation

Reference 38

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This paper cites Per- ceptual losses for real-time style transfer and super- resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Per- ceptual losses for real-time style transfer and super- resolution

Reference 39

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Augmenting Perceptual Super-Resolution via Image Quality Predictors Denoising diffusion restoration models

Reference 40

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This paper cites MUSIQ: Multi-scale image qual- ity transformer.

Augmenting Perceptual Super-Resolution via Image Quality Predictors MUSIQ: Multi-scale image qual- ity transformer

Reference 41

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Augmenting Perceptual Super-Resolution via Image Quality Predictors E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles

Reference 42

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This paper cites Neural side-by-side: Predicting human preferences for no- reference super-resolution evaluation.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Neural side-by-side: Predicting human preferences for no- reference super-resolution evaluation

Reference 43

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This paper cites Deeply-recursive convolutional network for image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Deeply-recursive convolutional network for image super-resolution

Reference 44

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This paper cites Adversarial machine learning at scale.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Adversarial machine learning at scale

Reference 45

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This paper cites DataInf: Efficiently estimating data influence in LoRA-tuned LLMs and diffusion models.

Augmenting Perceptual Super-Resolution via Image Quality Predictors DataInf: Efficiently estimating data influence in LoRA-tuned LLMs and diffusion models

Reference 46

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This paper cites Deep Laplacian pyramid net- works for fast and accurate super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Deep Laplacian pyramid net- works for fast and accurate super-resolution

Reference 47

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

Augmenting Perceptual Super-Resolution via Image Quality Predictors Photo-realistic single image super- resolution using a generative adversarial network

Reference 48

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This paper cites Learning generative structure prior for blind text image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Learning generative structure prior for blind text image super-resolution

Reference 49

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This paper cites SwinIR: Image restoration using swin transformer.

Augmenting Perceptual Super-Resolution via Image Quality Predictors SwinIR: Image restoration using swin transformer

Reference 50

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Observation 0adf1332-5846-4866-9c3d-a1917a0c2628 · outbound

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

Augmenting Perceptual Super-Resolution via Image Quality Predictors Details or ar- tifacts: A locally discriminative learning approach to realistic image super-resolution

Reference 51

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Observation f912124e-e257-415d-9661-1219c1488354 · outbound

This paper cites Rich hu- man feedback for text-to-image generation.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Rich hu- man feedback for text-to-image generation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.713129Z

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Observation f3e1b764-fe16-4d57-acb8-5799f4b2b7cf · outbound

This paper cites Tracking meets LoRA: Faster training, larger model, stronger perfor- mance.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Tracking meets LoRA: Faster training, larger model, stronger perfor- mance

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.697773Z

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Observation 7596af93-0837-4c64-9565-7bbc45f30462 · outbound

This paper cites VMamba: Visual state space model.

Augmenting Perceptual Super-Resolution via Image Quality Predictors VMamba: Visual state space model

Reference 54

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This paper cites Defense against adversarial at- tacks on no-reference image quality models with gra- dient norm regularization.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Defense against adversarial at- tacks on no-reference image quality models with gra- dient norm regularization

Reference 55

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Observation 1a5ebc42-fc0e-4cc7-825e-732fe4a08d30 · outbound

This paper cites SRFlow: Learning the super- resolution space with normalizing flow.

Augmenting Perceptual Super-Resolution via Image Quality Predictors SRFlow: Learning the super- resolution space with normalizing flow

Reference 56

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Observation cde18505-932e-4edd-aaa9-024a581d01b5 · outbound

This paper cites Learning a no-reference quality metric for single-image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Learning a no-reference quality metric for single-image super-resolution

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.629681Z

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Observation 46618c48-b54f-4ca0-a96c-3f7398552fd8 · outbound

This paper cites Structure-preserving super resolution with gradient guidance.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Structure-preserving super resolution with gradient guidance

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.614239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 94c3d1b8-4c9a-403e-9ae7-14f5fda80228 · outbound

This paper cites No-reference image quality assessment in the spatial domain.

Augmenting Perceptual Super-Resolution via Image Quality Predictors No-reference image quality assessment in the spatial domain

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.598170Z

Source-reported events for the cited work

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Observation 840ff4a1-b33c-4c70-86c8-cd7018b127d4 · outbound

This paper cites Understanding SSIM.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Understanding SSIM

Reference 60

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Observation 2106e086-8dff-4ff3-94d3-169277656b24 · outbound

This paper cites Exploiting deep generative prior for versatile image restoration and manipulation.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Exploiting deep generative prior for versatile image restoration and manipulation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.581542Z

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Observation 166f76a3-f8d8-4886-9f78-8a0b5379e91f · outbound

This paper cites Content-aware local GAN for photo-realistic super- resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Content-aware local GAN for photo-realistic super- resolution

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.565314Z

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Observation 5faecb66-3a18-4c04-98b9-575a5f424444 · outbound

This paper cites Perception-oriented single image super-resolution using optimal objective estimation.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Perception-oriented single image super-resolution using optimal objective estimation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.546934Z

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Observation c4e5704b-899b-42de-b45d-f5e749b7bba0 · outbound

This paper cites Understanding mid-level rep- resentations in visual processing.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Understanding mid-level rep- resentations in visual processing

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.529328Z

Source-reported events for the cited work

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Observation f0ddc6be-9fae-46fa-a357-6d6d79df9f43 · outbound

This paper cites Perception viewed as an inverse problem.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Perception viewed as an inverse problem

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.512072Z

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Observation faa17b86-c3f7-4ac7-ab49-79040d100872 · outbound

This paper cites Color image database TID2013: Peculiarities and preliminary results.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Color image database TID2013: Peculiarities and preliminary results

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.496100Z

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Observation 54eea74e-7635-4686-919d-c8f8fad88d15 · outbound

This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T10:18:02.326120Z

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Observation c55a5cd6-7fde-4cab-bf5e-0bc780ee89ca · outbound

This paper cites A DCT statistics-based blind image quality index.

Augmenting Perceptual Super-Resolution via Image Quality Predictors A DCT statistics-based blind image quality index

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.479349Z

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Observation 1c8d4410-78d4-45fb-880d-893b38aa62ef · outbound

This paper cites Image super-resolution via iterative refine- ment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Image super-resolution via iterative refine- ment

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.463932Z

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Observation ed840bbe-a8af-4971-a74c-23e5be26195c · outbound

This paper cites Generative adver- sarial networks (GANs) challenges, solutions, and future directions.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Generative adver- sarial networks (GANs) challenges, solutions, and future directions

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.447699Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 83129689-420d-42a3-8f33-978beffbc35a · outbound

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

Augmenting Perceptual Super-Resolution via Image Quality Predictors Real-time single im- age and video super-resolution using an efficient sub- pixel convolutional neural network

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.431094Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f971ae3a-9a99-4f4a-850b-3a57abcf8e48 · outbound

This paper cites Blindly assess image quality in the wild guided by a self-adaptive hyper network.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Blindly assess image quality in the wild guided by a self-adaptive hyper network

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.414819Z

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Observation bd306682-9db5-4b83-bcad-067a961f9ecf · outbound

This paper cites Intriguing properties of neural networks.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Intriguing properties of neural networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.397972Z

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Observation 2da4156a-3ab7-4105-b675-d95400f243a9 · outbound

This paper cites NIMA: Neu- ral image assessment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors NIMA: Neu- ral image assessment

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.381328Z

Source-reported events for the cited work

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Observation 29d84230-4275-4f49-9190-ed364d392e78 · outbound

This paper cites Learning a blind measure of perceptual image qual- ity.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Learning a blind measure of perceptual image qual- ity

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.365321Z

Source-reported events for the cited work

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Observation fbf6ec85-35fd-40ee-9fdf-2122e7f0af63 · outbound

This paper cites Anchored neighborhood regression for fast example- based super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Anchored neighborhood regression for fast example- based super-resolution

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.348705Z

Source-reported events for the cited work

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Observation 6704c9c3-9b08-43d5-8929-578f520d271b · outbound

This paper cites A+: Adjusted anchored neighborhood regression for fast super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors A+: Adjusted anchored neighborhood regression for fast super-resolution

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.331702Z

Source-reported events for the cited work

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Observation 03dd0373-f039-4ebc-8a6b-f07d7c0018bc · outbound

This paper cites Wasserstein Auto-Encoders.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Wasserstein Auto-Encoders

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T10:18:02.387540Z

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Observation 2576fdbf-0541-4db6-878e-2ba44654ea1d · outbound

This paper cites Deep cyclic generative adversarial residual convo- lutional networks for real image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Deep cyclic generative adversarial residual convo- lutional networks for real image super-resolution

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.315354Z

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Observation eab2ed59-3292-484e-b9fe-c322b9ba1083 · outbound

This paper cites Methodol- ogy for the subjective assessment of the quality of television pictures.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Methodol- ogy for the subjective assessment of the quality of television pictures

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.299292Z

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Observation a3a9cbc2-af74-4ec8-ab89-dd55e75c2185 · outbound

This paper cites Blind image quality evaluation using perception based fea- tures.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Blind image quality evaluation using perception based fea- tures

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.283208Z

Source-reported events for the cited work

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Observation dc35a175-8f74-4e5c-979a-6af1d560c5d5 · outbound

This paper cites Exploring CLIP for assessing the look and feel of images.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Exploring CLIP for assessing the look and feel of images

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.267237Z

Source-reported events for the cited work

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Observation f75ed1fd-cd9c-4fb1-89bb-b873a6df3eb0 · outbound

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

Augmenting Perceptual Super-Resolution via Image Quality Predictors Exploiting diffusion prior for real-world image super-resolution

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.250444Z

Source-reported events for the cited work

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Observation ff6d4fdc-2ccc-432a-a19a-fd898d1913b4 · outbound

This paper cites ESRGAN: Enhanced super-resolution generative ad- versarial networks.

Augmenting Perceptual Super-Resolution via Image Quality Predictors ESRGAN: Enhanced super-resolution generative ad- versarial networks

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.234643Z

Source-reported events for the cited work

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Observation aa26a3fb-88c9-42c0-82d2-973c2caf7090 · outbound

This paper cites Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.219585Z

Source-reported events for the cited work

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Observation c9a8cd35-fcb2-4f2a-877f-51bf8c5ce594 · outbound

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

Augmenting Perceptual Super-Resolution via Image Quality Predictors Image quality assessment: from error visibility to structural similarity

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.201869Z

Source-reported events for the cited work

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Observation 1618a174-6589-4357-8b0d-22f857f9f791 · outbound

This paper cites Exploring video quality assessment on user generated contents from aesthetic 12 and technical perspectives.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Exploring video quality assessment on user generated contents from aesthetic 12 and technical perspectives

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.186319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 43229f8e-76d5-4264-af2a-67099b52470c · outbound

This paper cites Q- Align: Teaching LMMs for visual scoring via dis- crete text-defined levels.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Q- Align: Teaching LMMs for visual scoring via dis- crete text-defined levels

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.169957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8082e844-7bfd-4776-8339-330653500fd3 · outbound

This paper cites One-step effective diffusion network for real- world image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors One-step effective diffusion network for real- world image super-resolution

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.153539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 60d96eab-2d60-44f4-9e00-e7287b3369f3 · outbound

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

Augmenting Perceptual Super-Resolution via Image Quality Predictors SeeSR: Towards semantics-aware real-world image super-resolution

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.137299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a63d3ebd-6151-4ce2-adb4-ed69ecfe4fb0 · outbound

This paper cites DiffIR: Efficient diffusion model for im- age restoration.

Augmenting Perceptual Super-Resolution via Image Quality Predictors DiffIR: Efficient diffusion model for im- age restoration

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.120566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 70ab9f75-0d3a-4c76-9834-e2f67f672f1f · outbound

This paper cites ImageReward: Learning and evaluating human pref- erences for text-to-image generation.

Augmenting Perceptual Super-Resolution via Image Quality Predictors ImageReward: Learning and evaluating human pref- erences for text-to-image generation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.103294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dd41894e-0023-4f10-b9eb-71676b312114 · outbound

This paper cites Coupled dictionary train- ing for image super-resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Coupled dictionary train- ing for image super-resolution

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.087008Z

Source-reported events for the cited work

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Observation 060ac183-63dc-463b-84f4-f5e12089a46d · outbound

This paper cites MANIQA: Multi-dimension attention network for no-reference image quality assessment.

Augmenting Perceptual Super-Resolution via Image Quality Predictors MANIQA: Multi-dimension attention network for no-reference image quality assessment

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.071151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef6355b9-d7b7-4d89-8cbe-81843ac811f0 · outbound

This paper cites Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.054024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 797ec89a-1192-47c6-8f00-f7df1441d9fc · outbound

This paper cites Local implicit normalizing flow for arbitrary-scale image super- resolution.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Local implicit normalizing flow for arbitrary-scale image super- resolution

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.038231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 083f32da-6732-46dc-ab0b-8ce3d091f877 · outbound

This paper cites From patches to pictures (PaQ-2-PiQ): Mapping the per- ceptual space of picture quality.

Augmenting Perceptual Super-Resolution via Image Quality Predictors From patches to pictures (PaQ-2-PiQ): Mapping the per- ceptual space of picture quality

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.021787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 84e1f7b3-6216-4c06-bd07-1f241c050294 · outbound

This paper cites A feature- enriched completely blind image quality evaluator.

Augmenting Perceptual Super-Resolution via Image Quality Predictors A feature- enriched completely blind image quality evaluator

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:03.004526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 61529bf1-7dbf-4402-841c-70ec086868b5 · outbound

This paper cites The unreasonable ef- fectiveness of deep features as a perceptual metric.

Augmenting Perceptual Super-Resolution via Image Quality Predictors The unreasonable ef- fectiveness of deep features as a perceptual metric

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:02.987171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c9480344-4743-4ea1-bbd3-f1c16940cb27 · outbound

This paper cites Blind image quality assessment using a deep bilinear convolutional neural network.

Augmenting Perceptual Super-Resolution via Image Quality Predictors Blind image quality assessment using a deep bilinear convolutional neural network

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:18:02.969611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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