Exploring Scalable Unified Modeling for General Low-Level Vision | Paper Citation Record

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This paper cites Ai in photography: Scrutinizing implementation of super- resolution techniques in photo-editors,.

Exploring Scalable Unified Modeling for General Low-Level Vision Ai in photography: Scrutinizing implementation of super- resolution techniques in photo-editors,

Reference 1

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This paper cites Cardiac image super-resolution with global correspondence using multi-atlas patchmatch,.

Exploring Scalable Unified Modeling for General Low-Level Vision Cardiac image super-resolution with global correspondence using multi-atlas patchmatch,

Reference 2

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This paper cites Sar image despeckling through convolutional neural networks,.

Exploring Scalable Unified Modeling for General Low-Level Vision Sar image despeckling through convolutional neural networks,

Reference 3

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This paper cites Visual prompt tuning,.

Exploring Scalable Unified Modeling for General Low-Level Vision Visual prompt tuning,

Reference 4

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This paper cites Sequential modeling enables scalable learning for large vision models,.

Exploring Scalable Unified Modeling for General Low-Level Vision Sequential modeling enables scalable learning for large vision models,

Reference 5

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This paper cites All-in-one image restoration for unknown corruption,.

Exploring Scalable Unified Modeling for General Low-Level Vision All-in-one image restoration for unknown corruption,

Reference 6

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This paper cites Promptir: Prompting for all-in-one image restoration,.

Exploring Scalable Unified Modeling for General Low-Level Vision Promptir: Prompting for all-in-one image restoration,

Reference 7

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This paper cites Visual prompting via image inpainting,.

Exploring Scalable Unified Modeling for General Low-Level Vision Visual prompting via image inpainting,

Reference 8

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Observation 8baa3431-879c-495b-8769-86d971854d18 · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning,.

Exploring Scalable Unified Modeling for General Low-Level Vision Images speak in images: A generalist painter for in-context visual learning,

Reference 9

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This paper cites Unifying image processing as visual prompting question answering,.

Exploring Scalable Unified Modeling for General Low-Level Vision Unifying image processing as visual prompting question answering,

Reference 10

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This paper cites Learning a low-level vision generalist via visual task prompt,.

Exploring Scalable Unified Modeling for General Low-Level Vision Learning a low-level vision generalist via visual task prompt,

Reference 11

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

Exploring Scalable Unified Modeling for General Low-Level Vision Learning a deep convolutional network for image super-resolution,

Reference 12

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This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,.

Exploring Scalable Unified Modeling for General Low-Level Vision Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,

Reference 13

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This paper cites Defocus deblurring using dual-pixel data,.

Exploring Scalable Unified Modeling for General Low-Level Vision Defocus deblurring using dual-pixel data,

Reference 14

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This paper cites Compression artifacts reduction by a deep convolutional network,.

Exploring Scalable Unified Modeling for General Low-Level Vision Compression artifacts reduction by a deep convolutional network,

Reference 15

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This paper cites Deep joint rain detection and removal from a single image,.

Exploring Scalable Unified Modeling for General Low-Level Vision Deep joint rain detection and removal from a single image,

Reference 16

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This paper cites Deep joint rain detection and removal from a single image,.

Exploring Scalable Unified Modeling for General Low-Level Vision Deep joint rain detection and removal from a single image,

Reference 17

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This paper cites A comprehensive overview of image enhancement techniques,.

Exploring Scalable Unified Modeling for General Low-Level Vision A comprehensive overview of image enhancement techniques,

Reference 18

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This paper cites Deep bilateral learning for real-time image enhancement,.

Exploring Scalable Unified Modeling for General Low-Level Vision Deep bilateral learning for real-time image enhancement,

Reference 19

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This paper cites Fast local laplacian filters: Theory and applications,.

Exploring Scalable Unified Modeling for General Low-Level Vision Fast local laplacian filters: Theory and applications,

Reference 20

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This paper cites Hdrunet: Single image hdr reconstruction with denoising and dequantization,.

Exploring Scalable Unified Modeling for General Low-Level Vision Hdrunet: Single image hdr reconstruction with denoising and dequantization,

Reference 21

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This paper cites Learning to see in the dark,.

Exploring Scalable Unified Modeling for General Low-Level Vision Learning to see in the dark,

Reference 22

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This paper cites A computational approach to edge detection,.

Exploring Scalable Unified Modeling for General Low-Level Vision A computational approach to edge detection,

Reference 23

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This paper cites Perceptual losses for real-time style transfer and super-resolution,.

Exploring Scalable Unified Modeling for General Low-Level Vision Perceptual losses for real-time style transfer and super-resolution,

Reference 24

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This paper cites Language models are few-shot learners,.

Exploring Scalable Unified Modeling for General Low-Level Vision Language models are few-shot learners,

Reference 25

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This paper cites The power of scale for parameter-efficient prompt tuning,.

Exploring Scalable Unified Modeling for General Low-Level Vision The power of scale for parameter-efficient prompt tuning,

Reference 26

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

Exploring Scalable Unified Modeling for General Low-Level Vision Lora: Low-rank adaptation of large language models,

Reference 27

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This paper cites Learning to prompt for vision- language models,.

Exploring Scalable Unified Modeling for General Low-Level Vision Learning to prompt for vision- language models,

Reference 28

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This paper cites A Preliminary Exploration Towards General Image Restoration.

Exploring Scalable Unified Modeling for General Low-Level Vision A Preliminary Exploration Towards General Image Restoration

Reference 29

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This paper cites Designing a practical degradation model for deep blind image super-resolution,.

Exploring Scalable Unified Modeling for General Low-Level Vision Designing a practical degradation model for deep blind image super-resolution,

Reference 30

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

Exploring Scalable Unified Modeling for General Low-Level Vision Real-esrgan: Training real-world blind super-resolution with pure synthetic data,

Reference 31

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This paper cites Unsu- pervised degradation representation learning for blind super-resolution,.

Exploring Scalable Unified Modeling for General Low-Level Vision Unsu- pervised degradation representation learning for blind super-resolution,

Reference 32

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This paper cites ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration.

Exploring Scalable Unified Modeling for General Low-Level Vision ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration

Reference 33

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This paper cites A comparative study of image restoration networks for general backbone network design,.

Exploring Scalable Unified Modeling for General Low-Level Vision A comparative study of image restoration networks for general backbone network design,

Reference 34

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This paper cites Restormer: Efficient transformer for high-resolution image restoration,.

Exploring Scalable Unified Modeling for General Low-Level Vision Restormer: Efficient transformer for high-resolution image restoration,

Reference 35

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

Exploring Scalable Unified Modeling for General Low-Level Vision Activating more pixels in image super-resolution transformer,

Reference 36

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

Exploring Scalable Unified Modeling for General Low-Level Vision High-resolution image synthesis with latent diffusion models,

Reference 37

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This paper cites Bayesian-based iterative method of image restora- tion,.

Exploring Scalable Unified Modeling for General Low-Level Vision Bayesian-based iterative method of image restora- tion,

Reference 38

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This paper cites Imagenet: A large-scale hierarchical image database,.

Exploring Scalable Unified Modeling for General Low-Level Vision Imagenet: A large-scale hierarchical image database,

Reference 39

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This paper cites Benchmarking single-image dehazing and beyond,.

Exploring Scalable Unified Modeling for General Low-Level Vision Benchmarking single-image dehazing and beyond,

Reference 40

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This paper cites Multi-scale progressive fusion network for single image deraining,.

Exploring Scalable Unified Modeling for General Low-Level Vision Multi-scale progressive fusion network for single image deraining,

Reference 41

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This paper cites Edge-preserving decompositions for multi-scale tone and detail manipulation,.

Exploring Scalable Unified Modeling for General Low-Level Vision Edge-preserving decompositions for multi-scale tone and detail manipulation,

Reference 42

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This paper cites A new journey from sdrtv to hdrtv,.

Exploring Scalable Unified Modeling for General Low-Level Vision A new journey from sdrtv to hdrtv,

Reference 43

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This paper cites Deep retinex decomposition for low-light enhancement,.

Exploring Scalable Unified Modeling for General Low-Level Vision Deep retinex decomposition for low-light enhancement,

Reference 44

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This paper cites Learning photo- graphic global tonal adjustment with a database of input/output image pairs,.

Exploring Scalable Unified Modeling for General Low-Level Vision Learning photo- graphic global tonal adjustment with a database of input/output image pairs,

Reference 45

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This paper cites An underwater image enhancement benchmark dataset and beyond,.

Exploring Scalable Unified Modeling for General Low-Level Vision An underwater image enhancement benchmark dataset and beyond,

Reference 46

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This paper cites Dense extreme inception network: Towards a robust cnn model for edge detection,.

Exploring Scalable Unified Modeling for General Low-Level Vision Dense extreme inception network: Towards a robust cnn model for edge detection,

Reference 47

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This paper cites The opencv library,.

Exploring Scalable Unified Modeling for General Low-Level Vision The opencv library,

Reference 48

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This paper cites Combining sketch and tone for pencil drawing production,.

Exploring Scalable Unified Modeling for General Low-Level Vision Combining sketch and tone for pencil drawing production,

Reference 49

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This paper cites Structure extraction from texture via relative total variation,.

Exploring Scalable Unified Modeling for General Low-Level Vision Structure extraction from texture via relative total variation,

Reference 50

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This paper cites Adaattn: Revisit attention mechanism in arbitrary neural style transfer,.

Exploring Scalable Unified Modeling for General Low-Level Vision Adaattn: Revisit attention mechanism in arbitrary neural style transfer,

Reference 51

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Observation 8bc2f95e-4e43-4345-9468-962216d9b9a5 · outbound

This paper cites Sars-cov- 2 ct-scan dataset: A large dataset of real patients ct scans for sars-cov-2 identification,.

Exploring Scalable Unified Modeling for General Low-Level Vision Sars-cov- 2 ct-scan dataset: A large dataset of real patients ct scans for sars-cov-2 identification,

Reference 52

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This paper cites Aid: A benchmark data set for performance evaluation of aerial scene classification,.

Exploring Scalable Unified Modeling for General Low-Level Vision Aid: A benchmark data set for performance evaluation of aerial scene classification,

Reference 53

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This paper cites Superbench: A super-resolution benchmark dataset for scientific machine learning,.

Exploring Scalable Unified Modeling for General Low-Level Vision Superbench: A super-resolution benchmark dataset for scientific machine learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:49.903534Z

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Observation 436b7c37-bf83-412e-b3f1-b1ab0283dfc0 · outbound

This paper cites Rellisur: A real low- light image super-resolution dataset,.

Exploring Scalable Unified Modeling for General Low-Level Vision Rellisur: A real low- light image super-resolution dataset,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:49.718301Z

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This paper cites Seal: A framework for systematic evaluation of real-world super- resolution,.

Exploring Scalable Unified Modeling for General Low-Level Vision Seal: A framework for systematic evaluation of real-world super- resolution,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:49.490497Z

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This paper cites Toward convolutional blind denoising of real photographs,.

Exploring Scalable Unified Modeling for General Low-Level Vision Toward convolutional blind denoising of real photographs,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:49.289662Z

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Observation d1162a9e-08da-422d-8ecd-5100a16025e0 · outbound

This paper cites Real-world blur dataset for learning and benchmarking deblurring algorithms,.

Exploring Scalable Unified Modeling for General Low-Level Vision Real-world blur dataset for learning and benchmarking deblurring algorithms,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:49.140104Z

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This paper cites Multi-stage progressive image restoration,.

Exploring Scalable Unified Modeling for General Low-Level Vision Multi-stage progressive image restoration,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:48.998828Z

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Observation 6d372725-19d9-4a21-975e-50983f4a0d45 · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study,.

Exploring Scalable Unified Modeling for General Low-Level Vision Ntire 2017 challenge on single image super-resolution: Dataset and study,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:48.832764Z

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Observation 9cf63c1b-3292-4d56-b4d2-a1ce0c3439ff · outbound

This paper cites Aid: A benchmark data set for performance evaluation of aerial scene classification,.

Exploring Scalable Unified Modeling for General Low-Level Vision Aid: A benchmark data set for performance evaluation of aerial scene classification,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:48.686378Z

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Observation 8a3e240d-e1ad-4006-b8cf-f1544bff6e36 · outbound

This paper cites Continuous remote sensing image super-resolution based on context interaction in implicit function space,.

Exploring Scalable Unified Modeling for General Low-Level Vision Continuous remote sensing image super-resolution based on context interaction in implicit function space,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:48.560945Z

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Observation fa1de669-6c2c-4087-813c-3e1d7ce336fd · outbound

This paper cites Discovering Distinctive "Semantics" in Super-Resolution Networks.

Exploring Scalable Unified Modeling for General Low-Level Vision Discovering Distinctive "Semantics" in Super-Resolution Networks

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:53:47.971367Z

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Observation c67ef59d-55f9-4d5d-9388-11ae5f7515da · outbound

This paper cites Revisiting the generalization problem of low-level vision models through the lens of image deraining,.

Exploring Scalable Unified Modeling for General Low-Level Vision Revisiting the generalization problem of low-level vision models through the lens of image deraining,

Reference 64

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Source-reported events for the cited work

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Observation 183cf5ad-84bb-46e5-9951-9c2f4c220850 · outbound

This paper cites Restoreagent: Autonomous image restoration agent via multimodal large language models,.

Exploring Scalable Unified Modeling for General Low-Level Vision Restoreagent: Autonomous image restoration agent via multimodal large language models,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:48.408530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e856d11-04f7-483a-8dbf-278ef230c474 · outbound

This paper cites Promptfix: You prompt and we fix the photo,.

Exploring Scalable Unified Modeling for General Low-Level Vision Promptfix: You prompt and we fix the photo,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:48.248187Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c1dff46-a606-4656-a43d-f121082486ec · outbound

This paper cites Pixwizard: Versatile image-to-image visual assistant with open-language instructions,.

Exploring Scalable Unified Modeling for General Low-Level Vision Pixwizard: Versatile image-to-image visual assistant with open-language instructions,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:53:48.128984Z

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