NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results | Paper Citation Record

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This paper cites Glean: Generative latent bank for large- factor image super-resolution.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Glean: Generative latent bank for large- factor image super-resolution

Reference 1

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Unresolved cited work

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This paper cites Real-world blind super-resolution via feature matching with implicit high- resolution priors.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Real-world blind super-resolution via feature matching with implicit high- resolution priors

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This paper cites Towards Real-World Blind Face Restoration with Generative Diffusion Prior.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Towards Real-World Blind Face Restoration with Generative Diffusion Prior

Reference 4

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This paper cites Fsrnet: End-to-end learning face super-resolution with facial priors.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Fsrnet: End-to-end learning face super-resolution with facial priors

Reference 5

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This paper cites NTIRE 2025 challenge on image super-resolution (×4): Methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on image super-resolution (×4): Methods and results

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This paper cites NTIRE 2025 challenge on real-world face restoration: Methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on real-world face restoration: Methods and results

Reference 7

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Unresolved cited work

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This paper cites NTIRE 2025 challenge on raw image restoration and super-resolution.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on raw image restoration and super-resolution

Reference 9

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This paper cites Raw image reconstruc- tion from RGB on smartphones.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Raw image reconstruc- tion from RGB on smartphones

Reference 10

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This paper cites ArcFace: Additive angular margin loss for deep face recognition.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results ArcFace: Additive angular margin loss for deep face recognition

Reference 11

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This paper cites TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution

Reference 12

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This paper cites NTIRE 2025 challenge on night photography rendering.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on night photography rendering

Reference 13

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This paper cites NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results

Reference 14

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Unresolved cited work

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This paper cites VQFR: Blind face restoration with vector-quantized dictionary and parallel de- coder.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results VQFR: Blind face restoration with vector-quantized dictionary and parallel de- coder

Reference 16

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This paper cites NTIRE 2025 challenge on text to image generation model quality assess- ment.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on text to image generation model quality assess- ment

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

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results KonIQ-10k: An ecologically valid database for deep learn- ing of blind image quality assessment

Reference 18

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This paper cites Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller

Reference 19

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This paper cites NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results

Reference 20

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This paper cites Progressive growing of GANs for improved quality, stability, and variation.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Progressive growing of GANs for improved quality, stability, and variation

Reference 21

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This paper cites A style-based generator architecture for generative adversarial networks.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results A style-based generator architecture for generative adversarial networks

Reference 22

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This paper cites MUSIQ: Multi-scale Image Quality Trans- former.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results MUSIQ: Multi-scale Image Quality Trans- former

Reference 23

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This paper cites Progressive face super-resolution via attention to facial landmark.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Progressive face super-resolution via attention to facial landmark

Reference 24

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This paper cites Adaface: Quality adaptive margin for face recognition.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Adaface: Quality adaptive margin for face recognition

Reference 25

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Larson and Damon M

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This paper cites NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results

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This paper cites NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results

Reference 28

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This paper cites NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Methods and results

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Ls- dir: A large scale dataset for image restoration

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Swinir: Image restoration us- ing swin transformer

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 the 2nd restore any image model (RAIM) in the wild challenge

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results KADID-10k: A large-scale artificially distorted IQA database

Reference 34

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This paper cites Diff- BIR: Towards blind image restoration with generative diffu- sion prior.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Diff- BIR: Towards blind image restoration with generative diffu- sion prior

Reference 35

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 XGC quality assessment chal- lenge: Methods and results

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on low light image enhancement: Methods and results

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This paper cites Waveface: Authentic face restoration with efficient frequency recovery.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Waveface: Authentic face restoration with efficient frequency recovery

Reference 38

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results DINOv2: Learning Robust Visual Features without Supervision

Reference 39

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This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 40

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This paper cites FFA-Net: Feature Fusion Attention Network for Single Image Dehazing.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

Reference 41

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This paper cites Diffbfr: Bootstrapping dif- fusion model for blind face restoration.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Diffbfr: Bootstrapping dif- fusion model for blind face restoration

Reference 42

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This paper cites The tenth NTIRE 2025 efficient super- resolution challenge report.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results The tenth NTIRE 2025 efficient super- resolution challenge report

Reference 43

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on UGC video enhancement: Meth- ods and results

Reference 44

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Adversarial diffusion distillation

Reference 45

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This paper cites Sheikh, Muhammad F.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Sheikh, Muhammad F

Reference 46

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This paper cites Deep semantic face deblurring.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Deep semantic face deblurring

Reference 47

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results zero-shot

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Very deep convo- lutional networks for large-scale image recognition

Reference 49

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This paper cites CLR-Face: Conditional latent refinement for blind face restoration using score-based diffusion models.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results CLR-Face: Conditional latent refinement for blind face restoration using score-based diffusion models

Reference 50

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This paper cites Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach

Reference 51

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This paper cites NTIRE 2025 challenge on event-based image deblurring: Methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on event-based image deblurring: Methods and results

Reference 52

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This paper cites The tenth ntire 2025 image denoising challenge report.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results The tenth ntire 2025 image denoising challenge report

Reference 53

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This paper cites Pixel-level and semantic-level adjustable super-resolution: A dual-lora approach.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Pixel-level and semantic-level adjustable super-resolution: A dual-lora approach

Reference 54

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Observation 1061c425-63ca-464f-9f09-6ea4cfa2f39f · outbound

This paper cites Overcoming false illusions in real-world face restora- tion with multi-modal guided diffusion model.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Overcoming false illusions in real-world face restora- tion with multi-modal guided diffusion model

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:52.240792Z

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This paper cites Dual associated encoder for face restoration.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Dual associated encoder for face restoration

Reference 56

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This paper cites NTIRE 2025 image shadow removal challenge report.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 image shadow removal challenge report

Reference 57

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This paper cites NTIRE 2025 ambi- ent lighting normalization challenge.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 ambi- ent lighting normalization challenge

Reference 58

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This paper cites Edict: Exact diffusion inversion via coupled transformations.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Edict: Exact diffusion inversion via coupled transformations

Reference 59

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This paper cites Ex- ploring clip for assessing the look and feel of images.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Ex- ploring clip for assessing the look and feel of images

Reference 60

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This paper cites Chan, and Chen Change Loy.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Chan, and Chen Change Loy

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:52.166713Z

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This paper cites One-step diffusion model for face restoration.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results One-step diffusion model for face restoration

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:52.055987Z

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This paper cites To- wards real-world blind face restoration with generative facial prior.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results To- wards real-world blind face restoration with generative facial prior

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.966813Z

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Observation a5c9c8bf-c487-41dc-8555-67dcfbf9652d · outbound

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

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.952670Z

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Observation 56d4b6e2-2ee2-47da-82ab-47f3313390a7 · outbound

This paper cites NTIRE 2025 challenge on light field image super-resolution: Methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on light field image super-resolution: Methods and results

Reference 65

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Restoreformer++: Towards real- world blind face restoration from undegraded key-value pairs

Reference 66

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This paper cites Dr2: Diffusion-based robust degradation remover for blind face restoration.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Dr2: Diffusion-based robust degradation remover for blind face restoration

Reference 67

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Observation a13fda30-834e-4fb3-8107-326fe9086c0f · outbound

This paper cites Q-align: Teaching lmms for visual scoring via discrete text-defined levels.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Q-align: Teaching lmms for visual scoring via discrete text-defined levels

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.788985Z

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Observation 40242c0c-5be0-40d8-809b-a92daf1710b7 · outbound

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

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results One-step effective diffusion network for real-world image super-resolution

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.774541Z

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Observation deca062a-e366-4970-83d6-7c4836c321bc · outbound

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

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results SeeSR: Towards semantics-aware real-world image super-resolution

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.760557Z

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Observation b12011f3-cc10-46e8-87ac-b33de81a6fd2 · outbound

This paper cites Learning degradation- unaware representation with prior-based latent transforma- tions for blind face restoration.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Learning degradation- unaware representation with prior-based latent transforma- tions for blind face restoration

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.648740Z

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Observation 216192b4-1329-4526-8f9c-22714acf06fb · outbound

This paper cites NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results

Reference 72

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This paper cites PGDiff: Guiding diffusion models for versatile face restoration via partial guidance.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results PGDiff: Guiding diffusion models for versatile face restoration via partial guidance

Reference 73

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

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results MANIQA: Multi-dimension attention network for no- reference image quality assessment

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.531677Z

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Observation bf016788-8359-4bc1-9620-39e49a781da4 · outbound

This paper cites Gan prior embedded network for blind face restoration in the wild.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Gan prior embedded network for blind face restoration in the wild

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T11:48:50.899008Z

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This paper cites Scaling up to excellence: Practicing model scaling for photo- realistic image restoration in the wild.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Scaling up to excellence: Practicing model scaling for photo- realistic image restoration in the wild

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.507606Z

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Observation 14e97792-dba6-43f4-a12d-cf9e54165217 · outbound

This paper cites Super-resolving very low-resolution face images with sup- plementary attributes.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Super-resolving very low-resolution face images with sup- plementary attributes

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.493027Z

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Observation 16565ed9-9c6f-46cd-9f8a-fe38c0406479 · outbound

This paper cites DifFace: Blind Face Restoration with Diffused Error Contraction.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results DifFace: Blind Face Restoration with Diffused Error Contraction

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T11:48:50.912706Z

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Observation 77bbcad0-69ea-41b0-9412-891e3ffb7115 · outbound

This paper cites NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T11:48:50.918171Z

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Observation f6109802-81db-436d-8bac-a01b6f701a24 · outbound

This paper cites InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-16T11:48:50.923529Z

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Observation a9f7fe92-61b4-472d-9f65-86798b38acb9 · outbound

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NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:48:51.459498Z

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Observation 7e638e22-fad0-4559-bdee-a73fab03a1d6 · outbound

This paper cites Blind image quality assessment via vision- language correspondence: A multitask learning perspective.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Blind image quality assessment via vision- language correspondence: A multitask learning perspective

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.445432Z

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Observation fc44cc73-19c6-49e3-b4f0-4b268ff98358 · outbound

This paper cites When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:48:51.173617Z

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Observation 5b4a8de6-bd4e-490a-9db8-011e1e650679 · outbound

This paper cites Chan, Chongyi Li, and Chen Change Loy.

NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results Chan, Chongyi Li, and Chen Change Loy

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:48:51.401201Z

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