From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration | Paper Citation Record

Observation 2b49f7c2-b8f5-441b-849b-a4ee3c456a62 · outbound

This paper cites Fast, efficient, and accu- rate neuro-imaging denoising via supervised deep learning,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Fast, efficient, and accu- rate neuro-imaging denoising via supervised deep learning,

Reference 1

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This paper cites Ad- vancing real-world image dehazing: Perspective, modules, and training,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Ad- vancing real-world image dehazing: Perspective, modules, and training,

Reference 2

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This paper cites Multiscale low-light image enhancement network with il- lumination constraint,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Multiscale low-light image enhancement network with il- lumination constraint,

Reference 3

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Observation c415d5d8-d452-44e7-ad1f-7a427ca9b8f1 · outbound

This paper cites Bridg- ing the gap between haze scenarios: A unified image dehaz- ing model,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Bridg- ing the gap between haze scenarios: A unified image dehaz- ing model,

Reference 4

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Observation d34b5cbe-c15f-401b-a681-14fb82e47aea · outbound

This paper cites A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends

Reference 5

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

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration A comprehensive review of deep learning-based real-world image restoration,

Reference 6

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Observation b81f28d6-7cb5-40f4-a551-eb2192f6c3f6 · outbound

This paper cites Vision language models in au- tonomous driving: A survey and outlook,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Vision language models in au- tonomous driving: A survey and outlook,

Reference 7

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Observation 37849a8b-b851-411d-a4f3-d3ac91eed20c · outbound

This paper cites Maui: modular analytics of uas imagery for specialty crop research,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Maui: modular analytics of uas imagery for specialty crop research,

Reference 8

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Observation 9bbeb06d-37c3-4f05-8d7d-1719ea0ed703 · outbound

This paper cites Vision- based real-time marine and offshore structural health mon- itoring system using underwater robots,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Vision- based real-time marine and offshore structural health mon- itoring system using underwater robots,

Reference 9

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Observation 4ad42ce9-3994-4061-b80e-827b72b8aead · outbound

This paper cites Development and external valida- tion of an artificial intelligence-based method for scalable chest radiograph diagnosis: A multi-country cross-sectional study,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Development and external valida- tion of an artificial intelligence-based method for scalable chest radiograph diagnosis: A multi-country cross-sectional study,

Reference 10

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Observation 761d8b73-271f-41c3-b7ee-0bc20da24722 · outbound

This paper cites Parallel driving with big models and foundation intelligence in cyber–physical–social spaces,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Parallel driving with big models and foundation intelligence in cyber–physical–social spaces,

Reference 11

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Observation 32ecf598-3752-4b60-8f5a-4cee69701eb8 · outbound

This paper cites Spatial- frequency dual-domain feature fusion network for low-light remote sensing image enhancement,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Spatial- frequency dual-domain feature fusion network for low-light remote sensing image enhancement,

Reference 12

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Observation a50819f3-a4fc-4e72-9a67-4b30157e2a37 · outbound

This paper cites Spatial residual for underwater object detection,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Spatial residual for underwater object detection,

Reference 13

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Observation ca037db6-635c-4a3c-b9d2-9be284749ad5 · outbound

This paper cites Energy-efficient high-fidelity image reconstruction with memristor arrays for medical di- agnosis,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Energy-efficient high-fidelity image reconstruction with memristor arrays for medical di- agnosis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:24.022961Z

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Observation 6baaf48c-94e7-490e-a756-9ccd44202bb1 · outbound

This paper cites Swinir: Image restoration using swin transformer,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Swinir: Image restoration using swin transformer,

Reference 15

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Observation 43f462ce-7a8a-4c96-9f94-35cc47c302fb · outbound

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

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Restormer: Efficient transformer for high-resolution image restoration,

Reference 16

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Observation ee48b7af-8806-4f35-87a4-1eab6b30421c · outbound

This paper cites Mambair: A simple baseline for image restoration with state-space model,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Mambair: A simple baseline for image restoration with state-space model,

Reference 17

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Observation 34e1b0af-1074-45d6-a396-1ee2492e88a4 · outbound

This paper cites Image restoration via frequency selection,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Image restoration via frequency selection,

Reference 18

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Observation 52b9afdb-789d-4d1b-9fa9-0cb5320d7d0f · outbound

This paper cites AdaIR: Adaptive all-in-one image restoration via fre- quency mining and modulation,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration AdaIR: Adaptive all-in-one image restoration via fre- quency mining and modulation,

Reference 19

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Observation a868463a-c196-4378-8de2-4414f485152a · outbound

This paper cites Prompt-based ingredient-oriented all-in-one image restora- tion,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Prompt-based ingredient-oriented all-in-one image restora- tion,

Reference 20

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Observation fddafacb-30ca-4268-8c93-58bbb2c8ee0e · outbound

This paper cites Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy

Reference 21

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Observation cd37d5b9-c3bc-407b-bde0-cf5e1ff16d28 · outbound

This paper cites Controlling Vision-Language Models for Multi-Task Image Restoration.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Controlling Vision-Language Models for Multi-Task Image Restoration

Reference 22

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Observation 9c02c765-7dc9-4ccf-8ff3-c74af4cb4dcb · outbound

This paper cites Real-world scene im- age enhancement with contrastive domain adaptation learn- ing,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Real-world scene im- age enhancement with contrastive domain adaptation learn- ing,

Reference 23

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Observation 3d20beb2-2716-40ae-84a9-daf9b7d1dd40 · outbound

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

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Promp- tir: Prompting for all-in-one image restoration,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:22.146230Z

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Observation 71df8c66-4f79-4b87-b095-d21b59e6938e · outbound

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

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration All-in-one image restoration for unknown corruption,

Reference 25

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Observation 8a247b80-786d-4399-a762-a85c4009f6df · outbound

This paper cites Multi- modal prompt perceiver: Empower adaptiveness generaliz- ability and fidelity for all-in-one image restoration,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Multi- modal prompt perceiver: Empower adaptiveness generaliz- ability and fidelity for all-in-one image restoration,

Reference 26

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This paper cites Tent: Fully test-time adaptation by entropy minimization,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Tent: Fully test-time adaptation by entropy minimization,

Reference 27

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Observation 2d0cee5f-e802-4d7c-91f8-0fdbc5240808 · outbound

This paper cites Adaptformer: Adapting vision transformers for scal- able visual recognition,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Adaptformer: Adapting vision transformers for scal- able visual recognition,

Reference 28

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Observation c17c7aeb-88fe-4c66-9c4c-4f4d28698c41 · outbound

This paper cites Test-Time Degradation Adaptation for Open-Set Image Restoration.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Test-Time Degradation Adaptation for Open-Set Image Restoration

Reference 29

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Observation 37155f56-b9d4-4386-a1a1-b9d8d74003f9 · outbound

This paper cites Continual test- time domain adaptation,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Continual test- time domain adaptation,

Reference 30

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Observation 3953c99b-f765-44d8-a68d-fb6b454b271f · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 31

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This paper cites Deep coral: Correlation alignment for deep domain adaptation,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Deep coral: Correlation alignment for deep domain adaptation,

Reference 32

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Observation e25a0bc4-a561-4109-93ab-a2ada7043c1c · outbound

This paper cites A high-quality denoising dataset for smartphone cameras,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration A high-quality denoising dataset for smartphone cameras,

Reference 33

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Observation d1a8cea9-e0c7-405b-903d-4af770af062a · outbound

This paper cites Benchmarking single-image dehazing and be- yond,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Benchmarking single-image dehazing and be- yond,

Reference 34

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This paper cites Toward Real-world Single Image Deraining: A New Benchmark and Beyond.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Toward Real-world Single Image Deraining: A New Benchmark and Beyond

Reference 35

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This paper cites Deep Retinex Decomposition for Low-Light Enhancement.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Deep Retinex Decomposition for Low-Light Enhancement

Reference 36

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

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration An underwater image enhancement benchmark dataset and beyond,

Reference 37

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Observation df4d4c4f-c457-4fbd-98ce-9eac68056dae · outbound

This paper cites Real-world Noisy Image Denoising: A New Benchmark.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Real-world Noisy Image Denoising: A New Benchmark

Reference 38

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Observation 88bb1f40-8616-4395-af14-69e3feb672c0 · outbound

This paper cites From sky to the ground: A large-scale benchmark and simple baseline towards real rain removal,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration From sky to the ground: A large-scale benchmark and simple baseline towards real rain removal,

Reference 39

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Observation c1472129-cd11-4e79-91b1-7b8dcc5646c0 · outbound

This paper cites Lime: Low-light image en- hancement via illumination map estimation,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Lime: Low-light image en- hancement via illumination map estimation,

Reference 40

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Observation bbcddf4a-9450-43d2-be45-25d93de24471 · outbound

This paper cites Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

Reference 41

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Observation ee266d9f-aaa1-4f9e-a263-5891ed4310ea · outbound

This paper cites Underwater camera: Improving visual perception via adap- tive dark pixel prior and color correction,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Underwater camera: Improving visual perception via adap- tive dark pixel prior and color correction,

Reference 42

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Observation 96702ca0-0b5d-4216-a450-d1568bb0fb22 · outbound

This paper cites Referenceless pre- diction of perceptual fog density and perceptual image de- fogging,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Referenceless pre- diction of perceptual fog density and perceptual image de- fogging,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:20.130535Z

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Observation 69e4b001-7aaa-4f74-b5b6-65b4290ada2e · outbound

This paper cites Real-world non-homogeneous haze removal by sliding self-attention wavelet network,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Real-world non-homogeneous haze removal by sliding self-attention wavelet network,

Reference 44

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Observation b1ad2c8f-6fa5-4a13-aa82-dca0420cf9c0 · outbound

This paper cites Making a “completely blind.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Making a “completely blind

Reference 45

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This paper cites Bilevel fast scene adaptation for low-light image enhance- ment,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Bilevel fast scene adaptation for low-light image enhance- ment,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:19.623641Z

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Observation a8f07be0-a19c-495e-87d0-89b3d93a25ba · outbound

This paper cites No-reference image quality assessment in the spatial domain,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration No-reference image quality assessment in the spatial domain,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:19.449212Z

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Observation 9fd333b5-ade9-45f8-bd0d-df240ee6ff4a · outbound

This paper cites Human-visual-system- inspired underwater image quality measures,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Human-visual-system- inspired underwater image quality measures,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:19.279745Z

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Observation a1bb15f5-9b76-4122-9b58-a5591fdfb60b · outbound

This paper cites An underwater color image qual- ity evaluation metric,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration An underwater color image qual- ity evaluation metric,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:19.132549Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cb7fbc2-2fc8-4b75-8921-8313b794d03e · outbound

This paper cites See through wa- ter: Heuristic modeling towards color correction for under- water image enhancement,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration See through wa- ter: Heuristic modeling towards color correction for under- water image enhancement,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:18.889698Z

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Observation 18b47b07-25d9-42be-9c3e-1fdc4f13e563 · outbound

This paper cites Rank-one prior: Real- time scene recovery,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Rank-one prior: Real- time scene recovery,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:18.701681Z

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Observation 1f752923-798f-4da7-bc28-730c0c11ee5f · outbound

This paper cites Selective hourglass mapping for universal im- age restoration based on diffusion model,.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Selective hourglass mapping for universal im- age restoration based on diffusion model,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:18.562299Z

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Observation 078b5270-b595-4285-9c6e-2642a33f5c00 · outbound

This paper cites Available: https://openreview.net/forum?id= uXl3bZLkr3c.

From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Available: https://openreview.net/forum?id= uXl3bZLkr3c

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:21.581247Z

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