Paper Citation Record · LEDGER

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2604.09313.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2604.09313 v1

Coverage vector

measured 42 of 42 reference resolution

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measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 1edc7f7a-0bc0-4cf4-ba62-cf2ec9210c36 · outbound

This paper cites Ultrahigh-resolution boreal forest canopy map- ping: Combining UA V imagery and photogrammetric point clouds in a deep-learning-based approach.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Ultrahigh-resolution boreal forest canopy map- ping: Combining UA V imagery and photogrammetric point clouds in a deep-learning-based approach

Reference 1

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Observation 271a17e8-02ab-4e8e-9657-192f71115f88 · outbound

This paper cites Dual convolutional neural networks for low-level vision.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Dual convolutional neural networks for low-level vision

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.853659Z

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Observation 2bc5615e-cd12-4fac-9e4b-a29ee2190f68 · outbound

This paper cites Impact of adverse weather and image distortions on vision-based UA V detection: A performance evaluation of deep learning models.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Impact of adverse weather and image distortions on vision-based UA V detection: A performance evaluation of deep learning models

Reference 3

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Observation dd616d54-966c-47e0-8b1c-c9990452f1c8 · outbound

This paper cites Dc-mamba: A degradation-aware cross-modality framework for blind super-resolution of thermal UA V images.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Dc-mamba: A degradation-aware cross-modality framework for blind super-resolution of thermal UA V images

Reference 4

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Observation 25744250-39a3-4604-a709-4f1027991c2b · outbound

This paper cites AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations

Reference 5

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Observation 95e43db5-3fa6-4fdd-be7b-d551a1ab9a44 · outbound

This paper cites YOLOv8: A novel object detection algo- rithm with enhanced performance and robustness.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark YOLOv8: A novel object detection algo- rithm with enhanced performance and robustness

Reference 6

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Observation 06cbfcff-4123-456c-8f94-dc494671b3b4 · outbound

This paper cites Dual-level prototype learning for composite degraded image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Dual-level prototype learning for composite degraded image restoration

Reference 7

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Observation 59542479-7e54-4f41-a055-f1aa71f05e55 · outbound

This paper cites A comprehensive survey and taxonomy on single image dehazing based on deep learning.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark A comprehensive survey and taxonomy on single image dehazing based on deep learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.825902Z

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Observation 15065cad-73de-4f66-a954-7c0a18bef7a7 · outbound

This paper cites A survey of deep learning-based low-light image enhancement.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark A survey of deep learning-based low-light image enhancement

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.802218Z

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Observation c7277d97-f6b0-4189-b8a5-9b8b46e6ed14 · outbound

This paper cites Phydae: Physics-guided degradation-adaptive experts for all-in-one remote sens- ing image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Phydae: Physics-guided degradation-adaptive experts for all-in-one remote sens- ing image restoration

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.795711Z

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Observation ad4c619c-eca5-4911-b6f5-1005cedd7cab · outbound

This paper cites Diffbir: Toward blind image restoration with generative diffusion prior.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Diffbir: Toward blind image restoration with generative diffusion prior

Reference 11

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This paper cites Moe-diffir: Task-customized diffusion priors for universal compressed image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Moe-diffir: Task-customized diffusion priors for universal compressed image restoration

Reference 12

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Observation 0469876a-ce27-4c8e-ba4b-62671c54655e · outbound

This paper cites UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity

Reference 13

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Observation abbdf276-5e54-4c47-9cb3-3fee01184ef4 · outbound

This paper cites Promptrestorer: A prompting image restoration method with degrada- tion perception.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Promptrestorer: A prompting image restoration method with degrada- tion perception

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.785153Z

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Observation f26d4718-e3c6-4afb-984d-59bedf654c0f · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Learning transferable visual models from natural language supervi- sion

Reference 15

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Observation 3a838263-c29f-4973-934d-9515a17bd6f0 · outbound

This paper cites A comprehensive review of deep learning-based real-world image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark A comprehensive review of deep learning-based real-world image restoration

Reference 16

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This paper cites Image dehaze algorithm based on improved atmospheric scattering models.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Image dehaze algorithm based on improved atmospheric scattering models

Reference 17

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Observation 32660ebb-a9cc-4cd2-a233-9864a4bf6377 · outbound

This paper cites A lightweight degradation-aware framework for robust object detection in adverse weather.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark A lightweight degradation-aware framework for robust object detection in adverse weather

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.839125Z

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Observation 893f3ee9-9059-481e-b8ab-f8bee6872529 · outbound

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

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Restormer: Efficient transformer for high-resolution image restoration

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.846144Z

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Observation 8920c14b-673a-4985-b0d9-bf93c731e0ca · outbound

This paper cites Vision transformers for single image dehazing.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Vision transformers for single image dehazing

Reference 20

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This paper cites Multi-expert adaptive selection: Task-balancing for all-in-one image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Multi-expert adaptive selection: Task-balancing for all-in-one image restoration

Reference 21

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

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Promptir: Prompting for all-in-one image restoration

Reference 22

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This paper cites Adair: Adaptive all-in-one image restoration via frequency mining and modulation.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Adair: Adaptive all-in-one image restoration via frequency mining and modulation

Reference 23

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Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark All-in-one image restoration for unknown corruption

Reference 24

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This paper cites Transweather: Transformer-based restoration of images degraded by adverse weather conditions.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Transweather: Transformer-based restoration of images degraded by adverse weather conditions

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.740787Z

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This paper cites Complexity experts are task-discriminative learners for any image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Complexity experts are task-discriminative learners for any image restoration

Reference 26

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This paper cites M2restore: Mixture-of-experts-based mamba-cnn fusion framework for all-in-one image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark M2restore: Mixture-of-experts-based mamba-cnn fusion framework for all-in-one image restoration

Reference 27

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Observation 0282c208-0b74-46be-a073-0fa7af4df6a4 · outbound

This paper cites Restoring spatially-heterogeneous distortions using mixture of experts network.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Restoring spatially-heterogeneous distortions using mixture of experts network

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.723914Z

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Observation 7f235228-be04-441b-bebd-e37bf613acda · outbound

This paper cites Towards unified image deblurring using a mixture-of-experts decoder.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Towards unified image deblurring using a mixture-of-experts decoder

Reference 29

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Observation 77dd9c05-3297-4c1b-a7fa-b8f96eeec556 · outbound

This paper cites Freprompter: Frequency self-prompt for all-in-one image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Freprompter: Frequency self-prompt for all-in-one image restoration

Reference 30

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Observation 28a512a2-1e3f-4914-a6ee-58d5e0ad2486 · outbound

This paper cites Selective frequency network for image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Selective frequency network for image restoration

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.752232Z

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Observation a4f12a3b-381a-4dfb-9bf2-a72fe1b17d40 · outbound

This paper cites Degradation-aware feature perturbation for all-in-one image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Degradation-aware feature perturbation for all-in-one image restoration

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.842473Z

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Observation efab7ea6-5e94-4678-8c2d-9189de9573ae · outbound

This paper cites Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding

Reference 33

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This paper cites Ffdnet: Toward a fast and flexible solution for CNN-based image denoising.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Ffdnet: Toward a fast and flexible solution for CNN-based image denoising

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.857803Z

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Observation dbaf1211-b0cd-46f3-aa26-5057190f49c7 · outbound

This paper cites Deep unfolding network for image super-resolution.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Deep unfolding network for image super-resolution

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.710798Z

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Observation 4c3d428d-b5e6-4003-90c1-b42ad8f28f3c · outbound

This paper cites Parame- ter efficient adaptation for image restoration with heterogeneous mixture- of-experts.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Parame- ter efficient adaptation for image restoration with heterogeneous mixture- of-experts

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.720367Z

Source-reported events for the cited work

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Observation 95ae7413-84b0-4d3c-aecb-a99c83da513e · outbound

This paper cites Promptcir: Blind compressed image restoration with prompt learning.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Promptcir: Blind compressed image restoration with prompt learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.706311Z

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Observation b4312ed1-787b-47f4-bc3d-cd2ff4d4be08 · outbound

This paper cites All-in-one multi- degradation image restoration network via hierarchical degradation rep- resentation.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark All-in-one multi- degradation image restoration network via hierarchical degradation rep- resentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.717331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 05b1d99c-04cb-4fc6-addc-ae255934901e · outbound

This paper cites Dpmambair: All-in-one image restoration via degradation-aware prompt state space model.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Dpmambair: All-in-one image restoration via degradation-aware prompt state space model

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:35:57.111873Z

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Observation 67256392-2eed-45f0-8635-76fffa7f6ed3 · outbound

This paper cites Universal Image Restoration Pre-training via Degradation Classification.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Universal Image Restoration Pre-training via Degradation Classification

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:35:57.099826Z

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Observation 0c5ba70c-6caa-4122-bf4a-d3ea43231680 · outbound

This paper cites Controlling vision- language model for enhancing image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Controlling vision- language model for enhancing image restoration

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T11:59:34.714253Z

Source-reported events for the cited work

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Observation 54aadd5d-a385-40c5-a504-5b50726e9e56 · outbound

This paper cites Vision-language model guided image restoration.

Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark Vision-language model guided image restoration

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:35:57.129961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

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