Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement | Paper Citation Record

Observation c61afd9b-913c-46c2-914d-681cb64b753b · outbound

This paper cites Remote sensing image scene classifi- cation: Benchmark and state of the art,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Remote sensing image scene classifi- cation: Benchmark and state of the art,

Reference 1

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Observation f9fadc79-219a-4c69-84c1-db4bcc8d229b · outbound

This paper cites Deep learning in remote sensing: A comprehensive review and list of resources,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Deep learning in remote sensing: A comprehensive review and list of resources,

Reference 2

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Observation ba897ac7-56b7-4e89-836d-c652bfc46192 · outbound

This paper cites Dota: A large-scale dataset for object detection in aerial images,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Dota: A large-scale dataset for object detection in aerial images,

Reference 3

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Observation 7df0ad8c-8927-48dd-829f-5bba625028a7 · outbound

This paper cites Atmospheric scattering model and non-uniform illumination compensation for low-light remote sensing image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Atmospheric scattering model and non-uniform illumination compensation for low-light remote sensing image enhancement,

Reference 4

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Observation 3c9ade46-51a6-45c7-ad20-af16a859a9e5 · outbound

This paper cites Ultra-high- definition low-light image enhancement: A benchmark and transformer- based method,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Ultra-high- definition low-light image enhancement: A benchmark and transformer- based method,

Reference 5

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Observation 41928d3e-d59a-435c-bbc0-9a694a353774 · outbound

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

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Deep retinex decomposition for low-light enhancement,

Reference 6

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Observation 34bdeae0-ca3d-4b98-b4b6-a0e5c612f8cd · outbound

This paper cites FourLLIE: Boosting low-light image enhancement by fourier frequency information,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement FourLLIE: Boosting low-light image enhancement by fourier frequency information,

Reference 7

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Observation 330326fa-cab5-4285-878b-7e4dc786c151 · outbound

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

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Spatial–frequency dual-domain feature fusion network for low-light remote sensing image enhancement,

Reference 8

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Observation aa710cd4-90be-4257-8e54-6f6d55bd64bd · outbound

This paper cites Low-light image and video enhancement using deep learning: A sur- vey,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Low-light image and video enhancement using deep learning: A sur- vey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.728899Z

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Observation b7f7af5b-1a1d-4a32-a627-b97cc6a85ba5 · outbound

This paper cites Toward fast, flexible, and robust low-light image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Toward fast, flexible, and robust low-light image enhancement,

Reference 10

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Observation 433e0ca9-1e3a-4066-a17b-87763195bde6 · outbound

This paper cites Uformer: A general u-shaped transformer for image restoration,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Uformer: A general u-shaped transformer for image restoration,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.709500Z

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Observation 0b2feb68-5e47-4de7-a762-9b68b785acd9 · outbound

This paper cites Hvi: A new color space for low-light image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Hvi: A new color space for low-light image enhancement,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.697334Z

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Observation 762da9db-0e0b-4180-8fc4-dc56aa6bf9de · outbound

This paper cites Structure- guided diffusion transformer for low-light image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Structure- guided diffusion transformer for low-light image enhancement,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.685648Z

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Observation 629d75bf-a804-421f-8d63-a0619bc7bef9 · outbound

This paper cites SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement

Reference 14

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Observation 80e35eb2-37c8-4a8e-b2f9-7b2e99aeec25 · outbound

This paper cites PixIE: Prompted Pixel-Space Low-Light Image Enhancement.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement PixIE: Prompted Pixel-Space Low-Light Image Enhancement

Reference 15

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Observation 37d87cf2-7036-4e76-a759-2598bcc1328f · outbound

This paper cites DINOv3.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement DINOv3

Reference 16

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Observation b18a187d-0584-4b95-98ce-2b1adb19280f · outbound

This paper cites Learning semantic-aware knowledge guidance for low-light image en- hancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Learning semantic-aware knowledge guidance for low-light image en- hancement,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.671578Z

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Observation 70a5b5dd-5b45-4c24-af90-6e5391917f2e · outbound

This paper cites Semantic-guided zero-shot learning for low- light image/video enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Semantic-guided zero-shot learning for low- light image/video enhancement,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.657265Z

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Observation 1d69d0c2-821c-4f0c-9f86-2f317ac2e323 · outbound

This paper cites Depth anything 3: Recovering the visual space from any views,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Depth anything 3: Recovering the visual space from any views,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.642965Z

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Observation 1dc55e69-0140-4343-b762-740e1a2c3154 · outbound

This paper cites Depth-aware blind image decomposition for real-world adverse weather recovery,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Depth-aware blind image decomposition for real-world adverse weather recovery,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.625740Z

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Observation 7459bb3a-48f0-4d6b-92cd-ba0c60109bb1 · outbound

This paper cites Zero- reference deep curve estimation for low-light image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Zero- reference deep curve estimation for low-light image enhancement,

Reference 21

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Observation 545923ec-db05-489d-b82e-b21154f271d0 · outbound

This paper cites QWR-Dec-Net: A quaternion-wavelet retinex framework for low-light image enhancement with applications to remote sensing,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement QWR-Dec-Net: A quaternion-wavelet retinex framework for low-light image enhancement with applications to remote sensing,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.597585Z

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Observation 814d2e95-b67f-46b8-8ed2-030eb5b69fe4 · outbound

This paper cites Boosting diffusion networks with deep external context-aware encoders for low-light image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Boosting diffusion networks with deep external context-aware encoders for low-light image enhancement,

Reference 23

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Observation 224f1cd8-0663-4fc6-9f09-af796afaf04e · outbound

This paper cites Spjfnet: Self-mining prior-guided joint frequency enhancement for ultra-efficient dark im- age restoration,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Spjfnet: Self-mining prior-guided joint frequency enhancement for ultra-efficient dark im- age restoration,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.568939Z

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Observation 65dd890f-7d71-47a4-aade-c8d406659543 · outbound

This paper cites Foundation models defining a new era in vision: A survey and outlook,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Foundation models defining a new era in vision: A survey and outlook,

Reference 25

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Observation e7de7ed6-5d47-496c-859c-8448345cac49 · outbound

This paper cites Edge- connect: Structure guided image inpainting using edge prediction,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Edge- connect: Structure guided image inpainting using edge prediction,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.547511Z

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Observation b8360b57-b7bc-4b0f-b1df-de384f7422e6 · outbound

This paper cites Training-Free Large Model Priors for Multiple-in-One Image Restoration.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Training-Free Large Model Priors for Multiple-in-One Image Restoration

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T22:34:21.082092Z

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Observation cf2ffaf6-a8f7-4959-8a9d-88c760bdc868 · outbound

This paper cites DSPFusion: Image fusion via degradation and semantic dual-prior guidance,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement DSPFusion: Image fusion via degradation and semantic dual-prior guidance,

Reference 28

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Observation 59b48e8b-7cb4-46e9-ac9c-712425b5394f · outbound

This paper cites TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T22:34:21.089761Z

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Observation c7b3bedf-2612-4ddb-92dd-bb0887bbe390 · outbound

This paper cites Multiprior learning via neural architecture search for blind face restoration,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Multiprior learning via neural architecture search for blind face restoration,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.523148Z

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Observation 3459a242-4bdc-4a75-ba0a-c2dce4c3b408 · outbound

This paper cites Lersgan: A gan-based model for low-light remote sensing image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Lersgan: A gan-based model for low-light remote sensing image enhancement,

Reference 31

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Observation c32946d2-eee1-4d3b-bb30-3c499a3d9fe3 · outbound

This paper cites Deepspg: Exploring deep semantic prior guidance for low-light image enhancement with multi- modal learning,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Deepspg: Exploring deep semantic prior guidance for low-light image enhancement with multi- modal learning,

Reference 32

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Observation 84e3e17e-f38f-4234-a088-d229e75afb3e · outbound

This paper cites Uretinex- net: Retinex-based deep unfolding network for low-light image enhance- ment,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Uretinex- net: Retinex-based deep unfolding network for low-light image enhance- ment,

Reference 33

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Observation 930bc021-b883-4fc6-8793-23d4d1edcda4 · outbound

This paper cites Empowering low-light image enhancer through customized learnable priors,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Empowering low-light image enhancer through customized learnable priors,

Reference 34

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Observation 753616e6-b57f-43d8-9704-81092fafbde7 · outbound

This paper cites Learning to adapt to light,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Learning to adapt to light,

Reference 35

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Observation dee65c3e-0bf3-4cc4-b2d3-efb847654a07 · outbound

This paper cites Implicit neural repre- sentation for cooperative low-light image enhancement,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Implicit neural repre- sentation for cooperative low-light image enhancement,

Reference 36

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Observation 39c12ed6-cb9f-478c-bc10-8e668b869c77 · outbound

This paper cites Learning a simple low-light image enhancer from paired low-light instances,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Learning a simple low-light image enhancer from paired low-light instances,

Reference 37

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Observation a86d47f2-90b5-4c29-add3-1d9748f8e732 · outbound

This paper cites Low-light image enhancement via generative perceptual priors,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Low-light image enhancement via generative perceptual priors,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.424800Z

Source-reported events for the cited work

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Observation 9f4a70a1-dc1a-48b7-aa22-e2f0ffe86e11 · outbound

This paper cites Bayesian neural networks for one-to-many mapping in image enhance- ment,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Bayesian neural networks for one-to-many mapping in image enhance- ment,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.412332Z

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Observation 14310601-351e-402e-be1c-9d33024e32a3 · outbound

This paper cites Bag-of-visual-words and spatial extensions for land-use classification,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Bag-of-visual-words and spatial extensions for land-use classification,

Reference 40

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Observation 81f6bdc2-97a6-406f-8add-1215b21b51d3 · outbound

This paper cites R2rnet: Low-light image enhancement via real-low to real-normal network,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement R2rnet: Low-light image enhancement via real-low to real-normal network,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.384876Z

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Observation 6e8f49ab-cd25-499a-9cda-22624039497f · outbound

This paper cites Unsupervised Ultra-High-Resolution UAV Low-Light Image Enhancement: A Benchmark, Metric and Framework.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Unsupervised Ultra-High-Resolution UAV Low-Light Image Enhancement: A Benchmark, Metric and Framework

Reference 42

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Observation 386e12fa-76a9-4000-b0eb-1ea053210333 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Image quality assessment: from error visibility to structural similarity,

Reference 43

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Observation 4a68a451-df28-4c14-aa02-c2b0de475791 · outbound

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

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement The unreasonable effectiveness of deep features as a perceptual metric,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T22:34:21.150198Z

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Observation 8d50b612-b4ab-4a02-83df-830aee96415b · outbound

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

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement No-reference image quality assessment in the spatial domain,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T22:34:21.154314Z

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Observation 10bb4e1a-201f-4125-99be-e6443559903d · outbound

This paper cites Making a completely blind image quality analyzer,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Making a completely blind image quality analyzer,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T22:34:21.157833Z

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Observation 5f7c1d45-e59f-4ae9-9c9b-5b61d519921a · outbound

This paper cites Blind image quality evaluation using perception based features,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Blind image quality evaluation using perception based features,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.340479Z

Source-reported events for the cited work

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Observation 0f50396a-2c37-4e1e-bedc-223c25f2748b · outbound

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

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Lime: Low-light image enhancement via illumination map estimation,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T22:34:21.165079Z

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Observation 7691038c-db8b-4c5f-b379-031127bc8446 · outbound

This paper cites Learning to enhance low-light image via zero-reference deep curve estimation,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Learning to enhance low-light image via zero-reference deep curve estimation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.318834Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ee1a7e75-2df4-4aef-9b02-e4cb7cd1e873 · outbound

This paper cites Poly kernel inception network for remote sensing detection,.

Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement Poly kernel inception network for remote sensing detection,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:34:21.305795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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