TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution | Paper Citation Record

Observation 3539dcb5-f807-45a6-a363-29c33039fb66 · outbound

This paper cites Dream- clear: High-capacity real-world image restoration with privacy-safe dataset curation.Advances in Neural Informa- tion Processing Systems, 37:55443–55469, 2024.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Dream- clear: High-capacity real-world image restoration with privacy-safe dataset curation.Advances in Neural Informa- tion Processing Systems, 37:55443–55469, 2024

Reference 1

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Observation 118fa68b-0cd4-4127-9d9f-24d150490879 · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Deep ViT Features as Dense Visual Descriptors

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:07:29.960791Z

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Observation 818b6580-394d-4a2f-906f-2de3de7cd40c · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Toward real-world single image super-resolution: A new benchmark and a new model

Reference 3

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Observation 71424b34-104c-4253-94fe-a03bd5167d66 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Toward real-world single image super-resolution: A new benchmark and a new model

Reference 4

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Observation 55f3260e-05b0-4e65-8ab7-9c905e2a147a · outbound

This paper cites Real-world single image super-resolution: A brief review.Information Fusion, 79:124–145, 2022.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Real-world single image super-resolution: A brief review.Information Fusion, 79:124–145, 2022

Reference 5

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Observation 11307584-128a-4c88-89db-752cee8bd790 · outbound

This paper cites Frequency-dynamic attention modulation for dense prediction.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Frequency-dynamic attention modulation for dense prediction

Reference 6

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Observation 0f87ea92-46a1-4cd0-8ea1-572dc61d7885 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Learning a deep convolutional network for image super-resolution

Reference 7

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Observation c3411f05-b342-49c6-986e-b4d3fb983122 · outbound

This paper cites Image super-resolution using deep convolutional net- works.IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307, 2015.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Image super-resolution using deep convolutional net- works.IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307, 2015

Reference 8

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Observation b599ddb2-6542-4332-ad1b-547aab80bcad · outbound

This paper cites arXiv preprint arXiv:2601.14161 (2026) 10.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution arXiv preprint arXiv:2601.14161 (2026) 10

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:07:29.966255Z

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Observation d60c9dac-b035-4a02-8c35-ce1332175ccc · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation dc0f83be-77e5-4238-b7aa-98ad2fb0ac0b · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 2dbd28a8-72de-4994-a428-925441c6f2a6 · outbound

This paper cites Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 04b064aa-453e-4767-993b-24e603fd29ec · outbound

This paper cites Do Vision Transformers See Like Humans? Evaluating their Perceptual Alignment.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Do Vision Transformers See Like Humans? Evaluating their Perceptual Alignment

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:07:29.963266Z

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Observation ac094ea1-2760-42c7-af3b-f8d33eddda88 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation a5d092c7-f233-4851-98de-0c405d67c349 · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 6078a6c9-2819-411c-9ffd-fab698bd6dea · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 16

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Observation 71dce50d-8ca3-4d9b-bf7c-f7ae874491b7 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution A style-based generator architecture for generative adversarial networks

Reference 17

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Observation 462357ad-f5cc-487e-8c00-e7033f682583 · outbound

This paper cites Musiq: Multi-scale image quality transformer.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Musiq: Multi-scale image quality transformer

Reference 18

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Observation 3d2cc17e-bd5b-4217-8726-cfa54cba7c34 · outbound

This paper cites Auto-Encoding Variational Bayes.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Auto-Encoding Variational Bayes

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-03T01:07:29.963610Z

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Observation 5f16260f-8a07-4115-80eb-5a9f7a3dd8e5 · outbound

This paper cites Flux.https://github.com/ black-forest-labs/flux, 2024.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Flux.https://github.com/ black-forest-labs/flux, 2024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 0d89921e-0eb3-4a72-b26e-5d777ee5f13b · outbound

This paper cites Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:07:29.970855Z

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Observation d3f47072-3a77-400c-b20e-7c903fa35ea8 · outbound

This paper cites One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:07:29.973322Z

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Observation 55004cbd-d565-4232-b8d8-af0808dfb22c · outbound

This paper cites Lsdir: A large scale dataset for image restoration.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Lsdir: A large scale dataset for image restoration

Reference 23

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Observation 1520e934-44f4-4d13-8fc8-2f3a953d4d7a · outbound

This paper cites Diff- bir: Toward blind image restoration with generative diffusion prior.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Diff- bir: Toward blind image restoration with generative diffusion prior

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation d5ffd667-aa08-4c8b-9694-a00218c1165a · outbound

This paper cites Decoupled weight de- cay regularization.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Decoupled weight de- cay regularization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 3aab9b69-a62f-43fe-872d-d2a7b05b8d83 · outbound

This paper cites Tiled diffusion.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Tiled diffusion

Reference 26

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Observation 1d6b6698-92b1-4660-bb33-ca1590832916 · outbound

This paper cites completely blind.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution completely blind

Reference 27

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Observation 8feb4bf5-9979-4183-ac63-656b837a7ffd · outbound

This paper cites Diffusion models, image super-resolution, and everything: A survey.IEEE Transactions on Neural Networks and Learn- ing Systems, 2024.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Diffusion models, image super-resolution, and everything: A survey.IEEE Transactions on Neural Networks and Learn- ing Systems, 2024

Reference 28

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Observation f7e136a1-f481-4924-8975-4a27eaf3d862 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution High-resolution image synthesis with latent diffusion models

Reference 29

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Observation 6e8ae93e-cd2a-4c23-b559-e2c5de161ef4 · outbound

This paper cites an unresolved cited work.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Unresolved cited work

Reference 30

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Observation 351c01bc-32b5-4f3c-916b-d862cdc0f37c · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Pixel-level and semantic-level ad- justable super-resolution: A dual-lora approach

Reference 31

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Observation 634d7f03-5a9e-421a-82e3-83c3f0d30bf4 · outbound

This paper cites Nima: Neural image assessment.IEEE transactions on image processing, 27(8): 3998–4011, 2018.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Nima: Neural image assessment.IEEE transactions on image processing, 27(8): 3998–4011, 2018

Reference 32

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Observation 5e64fbd0-cfcd-4fc4-b9cd-baf1c75b7064 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Ex- ploring clip for assessing the look and feel of images

Reference 33

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Observation c609a81d-6ca4-4979-849b-dc1125c1848b · outbound

This paper cites Exploiting diffusion prior for real-world image super-resolution.International Journal of Computer Vision, 132(12):5929–5949, 2024.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Exploiting diffusion prior for real-world image super-resolution.International Journal of Computer Vision, 132(12):5929–5949, 2024

Reference 34

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Observation 7d350904-df9f-4875-957a-3376474318c2 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Real-esrgan: Training real-world blind super-resolution with pure synthetic data

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Observation cf59f1f2-25c1-46a5-a973-a445ace342f8 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Sinsr: diffusion-based image super- resolution in a single step

Reference 36

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Observation 7dc0d67e-758a-400a-bba8-dbf1942940d1 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

Reference 37

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Observation c11da720-8235-4e18-9682-2278d8b6449d · outbound

This paper cites Deep learn- ing for image super-resolution: A survey.IEEE transactions on pattern analysis and machine intelligence, 43(10):3365– 3387, 2020.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Deep learn- ing for image super-resolution: A survey.IEEE transactions on pattern analysis and machine intelligence, 43(10):3365– 3387, 2020

Reference 38

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Observation 7de5d3ed-25cd-4325-bf6a-40e2418faa5a · outbound

This paper cites Component divide-and-conquer for real-world image super-resolution.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Component divide-and-conquer for real-world image super-resolution

Reference 39

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Observation 12111b6d-9d1c-4c6c-ac1f-616fd2cba238 · outbound

This paper cites One-step effective diffusion network for real-world image super-resolution.Advances in Neural Information Process- ing Systems, 37:92529–92553, 2024.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution One-step effective diffusion network for real-world image super-resolution.Advances in Neural Information Process- ing Systems, 37:92529–92553, 2024

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 70c12e80-3aab-4eb8-b006-294c72e08461 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Seesr: Towards semantics- aware real-world image super-resolution

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Observation 6e8c2cee-2263-4740-a0d8-537ee680b4a9 · outbound

This paper cites Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions

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Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 102e21f6-1f10-4f91-91f6-80bd90bbdb9b · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Maniqa: Multi-dimension attention network for no-reference image quality assessment

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Observation caa58353-6521-42da-b575-eb3b9a3e744e · outbound

This paper cites Resshift: Efficient diffusion model for image super- resolution by residual shifting.Advances in Neural Infor- mation Processing Systems, 36:13294–13307, 2023.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Resshift: Efficient diffusion model for image super- resolution by residual shifting.Advances in Neural Infor- mation Processing Systems, 36:13294–13307, 2023

Reference 44

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Observation e0b520ae-c6cc-42c7-90dd-82d9ac0424a6 · outbound

This paper cites Arbitrary-steps image super-resolution via diffusion inver- sion.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Arbitrary-steps image super-resolution via diffusion inver- sion

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 06ea7ede-aa80-48a4-8ca4-6aa7d133a1a8 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Adding conditional control to text-to-image diffusion models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation 1d3a0b35-b070-44a4-8013-69ab26ee14ba · outbound

This paper cites Making convolutional networks shift- invariant again.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Making convolutional networks shift- invariant again

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Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation c65e8626-787e-45f5-a258-70f8fe99f9aa · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution The unreasonable effectiveness of deep features as a perceptual metric

Reference 48

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Observation 97f84eda-0bb9-4b07-9079-5e0f229194e7 · outbound

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

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution Blind image quality assessment via vision- language correspondence: A multitask learning perspective

Reference 49

Resolution: unresolved; no resolver link, observed 2026-06-27T16:50:03.921332Z

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Observation e3c91173-3b55-4e98-81f0-90c7b63c04a7 · outbound

This paper cites The quality of the image generated byM8is also significantly lower than that ofM4N2, whileN8achieves the worst results.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution The quality of the image generated byM8is also significantly lower than that ofM4N2, whileN8achieves the worst results

Reference 50

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Observation 407f23ca-0c9f-40fd-bb8d-3aa4861dd34f · outbound

This paper cites TUDSR-S exhibits overwhelming perfor- mance across these one-step models, highlighting the effec- tiveness of our twice upsampling-diffusion method.

TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution TUDSR-S exhibits overwhelming perfor- mance across these one-step models, highlighting the effec- tiveness of our twice upsampling-diffusion method

Reference 51

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