Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:53:47.609817Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2507.14801.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:53:47.609817Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
67 of 67 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 8fbae9a7-3652-4a9e-9a97-9c7512a89a96 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Ai in photography: Scrutinizing implementation of super- resolution techniques in photo-editors,
Reference 1
Source-reported events for the cited work
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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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Exploring Scalable Unified Modeling for General Low-Level Vision Sar image despeckling through convolutional neural networks,
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Exploring Scalable Unified Modeling for General Low-Level Vision Visual prompt tuning,
Reference 4
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Exploring Scalable Unified Modeling for General Low-Level Vision Sequential modeling enables scalable learning for large vision models,
Reference 5
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Exploring Scalable Unified Modeling for General Low-Level Vision All-in-one image restoration for unknown corruption,
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Exploring Scalable Unified Modeling for General Low-Level Vision Promptir: Prompting for all-in-one image restoration,
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Exploring Scalable Unified Modeling for General Low-Level Vision Visual prompting via image inpainting,
Reference 8
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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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Observation 55510b9e-dd04-4498-a693-0dc2f3b4028e · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Unifying image processing as visual prompting question answering,
Reference 10
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Observation 8b5b0e26-9c89-43cf-94fa-b0a2301748de · outbound
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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Observation 26ecb2ce-36ce-4e99-b0e7-82f7aef42918 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Learning a deep convolutional network for image super-resolution,
Reference 12
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Observation f7efc61f-c357-4006-93f5-3d83bbcd4018 · outbound
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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Observation e01da6e0-3d4e-4711-93c3-96a8eff1149f · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Defocus deblurring using dual-pixel data,
Reference 14
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Observation b4d98b22-2d32-4f12-80c7-7b4be437d898 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Compression artifacts reduction by a deep convolutional network,
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Observation 563372fd-5a3f-410d-b814-c3446aff21f1 · outbound
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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Observation 0feb9b8a-6d56-4d13-bc0e-55397fee96ee · outbound
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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Observation 3ff4ba56-84d7-4c13-be75-25d21f2e2685 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision A comprehensive overview of image enhancement techniques,
Reference 18
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Exploring Scalable Unified Modeling for General Low-Level Vision Deep bilateral learning for real-time image enhancement,
Reference 19
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Exploring Scalable Unified Modeling for General Low-Level Vision Fast local laplacian filters: Theory and applications,
Reference 20
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Exploring Scalable Unified Modeling for General Low-Level Vision Hdrunet: Single image hdr reconstruction with denoising and dequantization,
Reference 21
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Observation 33604422-3383-4778-b4be-d130b4a927bd · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Learning to see in the dark,
Reference 22
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Exploring Scalable Unified Modeling for General Low-Level Vision A computational approach to edge detection,
Reference 23
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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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Observation 66d631e2-9a07-46db-9a1a-7e08704c1b04 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Language models are few-shot learners,
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Observation d113340c-0bde-4b0d-ba8d-54920133b65e · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision The power of scale for parameter-efficient prompt tuning,
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Observation 1d067692-8ef5-4ccd-a544-95696d212ae7 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Lora: Low-rank adaptation of large language models,
Reference 27
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Observation 9563b417-6423-47ca-8b47-db7f4ff3aaa5 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Learning to prompt for vision- language models,
Reference 28
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Observation ccc1ab6c-2802-4f73-ade5-5e745c951df9 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision A Preliminary Exploration Towards General Image Restoration
Reference 29
Source-reported events for the cited work
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Observation 6c610707-8a14-4240-b38f-a12a47048006 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Designing a practical degradation model for deep blind image super-resolution,
Reference 30
Source-reported events for the cited work
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Observation e9bc7a8f-eb8f-4b9a-bfaf-cc55ff19950b · outbound
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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Exploring Scalable Unified Modeling for General Low-Level Vision Unsu- pervised degradation representation learning for blind super-resolution,
Reference 32
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Observation f742de80-0f1c-46a1-9b4e-28b0a1147e38 · outbound
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Reference 33
Source-reported events for the cited work
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Observation a0287109-aa69-4252-8cbc-6ef3be0c50b5 · outbound
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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Observation d7b5eefb-0d8a-4004-b306-3d47f72e915e · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Restormer: Efficient transformer for high-resolution image restoration,
Reference 35
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Exploring Scalable Unified Modeling for General Low-Level Vision Activating more pixels in image super-resolution transformer,
Reference 36
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Observation ea1916df-f94b-45ea-8f5f-d4420a9fd973 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision High-resolution image synthesis with latent diffusion models,
Reference 37
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Observation def46a4b-7c81-4289-ac24-0c11a0ecd693 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Bayesian-based iterative method of image restora- tion,
Reference 38
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Observation 53ebd618-20da-4d6c-9956-73b9422a373c · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Imagenet: A large-scale hierarchical image database,
Reference 39
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Exploring Scalable Unified Modeling for General Low-Level Vision Benchmarking single-image dehazing and beyond,
Reference 40
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Exploring Scalable Unified Modeling for General Low-Level Vision Multi-scale progressive fusion network for single image deraining,
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Exploring Scalable Unified Modeling for General Low-Level Vision Edge-preserving decompositions for multi-scale tone and detail manipulation,
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Exploring Scalable Unified Modeling for General Low-Level Vision A new journey from sdrtv to hdrtv,
Reference 43
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Exploring Scalable Unified Modeling for General Low-Level Vision Deep retinex decomposition for low-light enhancement,
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Exploring Scalable Unified Modeling for General Low-Level Vision An underwater image enhancement benchmark dataset and beyond,
Reference 46
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Exploring Scalable Unified Modeling for General Low-Level Vision Dense extreme inception network: Towards a robust cnn model for edge detection,
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Reference 48
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Reference 49
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Exploring Scalable Unified Modeling for General Low-Level Vision Adaattn: Revisit attention mechanism in arbitrary neural style transfer,
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Reference 57
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Source-reported events for the cited work
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Reference 59
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Exploring Scalable Unified Modeling for General Low-Level Vision Aid: A benchmark data set for performance evaluation of aerial scene classification,
Reference 61
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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
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Exploring Scalable Unified Modeling for General Low-Level Vision Discovering Distinctive "Semantics" in Super-Resolution Networks
Reference 63
Source-reported events for the cited work
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Observation c67ef59d-55f9-4d5d-9388-11ae5f7515da · outbound
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
Source-reported events for the cited work
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Observation 183cf5ad-84bb-46e5-9951-9c2f4c220850 · outbound
Exploring Scalable Unified Modeling for General Low-Level Vision Restoreagent: Autonomous image restoration agent via multimodal large language models,
Reference 65
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Exploring Scalable Unified Modeling for General Low-Level Vision Promptfix: You prompt and we fix the photo,
Reference 66
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Exploring Scalable Unified Modeling for General Low-Level Vision Pixwizard: Versatile image-to-image visual assistant with open-language instructions,
Reference 67
Source-reported events for the cited work
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No inbound Pith citation observations are available.