Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement | Paper Citation Record

Observation 4dfb9f2f-1981-47d3-8bad-656d00fee005 · outbound

This paper cites Computer Modeling and the Design of Optimal Underwater Imaging Systems,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Computer Modeling and the Design of Optimal Underwater Imaging Systems,

Reference 1

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This paper cites Sea-thru: A method for removing water from underwater images,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Sea-thru: A method for removing water from underwater images,

Reference 2

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This paper cites Underwater Image Enhancement by Wavelength Compensation and Dehazing,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater Image Enhancement by Wavelength Compensation and Dehazing,

Reference 3

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This paper cites Underwater Single Image Color Restoration using Haze-lines and a New Quantitative Dataset,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater Single Image Color Restoration using Haze-lines and a New Quantitative Dataset,

Reference 4

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This paper cites A Retinex-based Enhancing Approach for Single Underwater Image,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement A Retinex-based Enhancing Approach for Single Underwater Image,

Reference 5

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This paper cites UIECˆ 2-Net: CNN-based Underwater Image Enhancement using Wwo Color Space,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement UIECˆ 2-Net: CNN-based Underwater Image Enhancement using Wwo Color Space,

Reference 6

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This paper cites IACC: Cross-Illumination Awareness and Color Correction for Underwater Images Under Mixed Natural and Artificial Lighting,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement IACC: Cross-Illumination Awareness and Color Correction for Underwater Images Under Mixed Natural and Artificial Lighting,

Reference 7

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Observation 3a5f9e0a-ec64-479a-868d-6e5e8be9a663 · outbound

This paper cites Robust Underwater Image Enhancement with Cascaded Multi-level Sub-networks and Triple Attention Mechanism,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Robust Underwater Image Enhancement with Cascaded Multi-level Sub-networks and Triple Attention Mechanism,

Reference 8

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Observation c78ac4d9-7b6a-4cf4-ac66-1b22be578f52 · outbound

This paper cites Wavelet-based fourier infor- mation interaction with frequency diffusion adjustment for underwater image restoration,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Wavelet-based fourier infor- mation interaction with frequency diffusion adjustment for underwater image restoration,

Reference 9

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Observation 80d7fb98-7956-4a84-9d3e-7c0ac9e2dbb3 · outbound

This paper cites Synergistic multi- scale detail refinement via intrinsic supervision for underwater image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Synergistic multi- scale detail refinement via intrinsic supervision for underwater image enhancement,

Reference 10

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Observation ab294779-cb86-4fe2-9b81-cccd1848797d · outbound

This paper cites Task-friendly underwater image enhancement for machine vision applications,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Task-friendly underwater image enhancement for machine vision applications,

Reference 11

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Observation ed039b98-3893-4458-a7b1-0d4435f73a9d · outbound

This paper cites Fast underwater image enhance- ment for improved visual perception,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Fast underwater image enhance- ment for improved visual perception,

Reference 12

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Observation fec8395c-a380-4a04-b364-fdcd3a56e0f5 · outbound

This paper cites U-shape transformer for underwater image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement U-shape transformer for underwater image enhancement,

Reference 13

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Observation d48c5146-9057-4ed8-b838-0a45bfdca564 · outbound

This paper cites Contrastive Semi- supervised Learning for Underwater Image Restoration via Reliable Bank,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Contrastive Semi- supervised Learning for Underwater Image Restoration via Reliable Bank,

Reference 14

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Observation 7681c754-f206-4356-8983-3b719e11f49d · outbound

This paper cites Twin Adversarial Contrastive Learning for Underwater Image Enhancement and Beyond,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Twin Adversarial Contrastive Learning for Underwater Image Enhancement and Beyond,

Reference 15

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Observation c79f52af-e3b6-41e3-b98f-59f45aed8793 · outbound

This paper cites Underwater image enhancement via medium transmission-guided multi-color space embedding,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater image enhancement via medium transmission-guided multi-color space embedding,

Reference 16

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Observation 6a28088e-f12d-411b-b62c-beb5da42229d · outbound

This paper cites Underwater Image Enhancement based on Deep Learning and Image Formation Model.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater Image Enhancement based on Deep Learning and Image Formation Model

Reference 17

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Observation 7e64b46c-03e2-40e3-af8c-9d576cef01fb · outbound

This paper cites Towards real-time advancement of underwater visual quality with gan,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Towards real-time advancement of underwater visual quality with gan,

Reference 18

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Observation ae164d0b-879b-4d6d-b782-17b1d0281826 · outbound

This paper cites GUDCP: Gen- eralization of underwater dark channel prior for underwater image restoration,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement GUDCP: Gen- eralization of underwater dark channel prior for underwater image restoration,

Reference 19

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Observation 465ceae8-93b4-4964-b094-7f5952afae2b · outbound

This paper cites Transmission estimation in underwater single images,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Transmission estimation in underwater single images,

Reference 20

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Observation cb49bae4-38a9-4002-9e48-73e07977f6ae · outbound

This paper cites Underwater image restoration based on image blurriness and light absorption,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater image restoration based on image blurriness and light absorption,

Reference 21

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Observation 08441d59-ea43-453d-886c-530604bc6bde · outbound

This paper cites Single underwater image restoration using adaptive attenuation-curve prior,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Single underwater image restoration using adaptive attenuation-curve prior,

Reference 22

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Observation d1305ec7-44ec-488b-95f6-07cf56d9cc75 · outbound

This paper cites Single underwater image restoration by decomposing curves of attenuating color,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Single underwater image restoration by decomposing curves of attenuating color,

Reference 23

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Observation 5fbf6bd3-031c-402a-ac55-4457234d933b · outbound

This paper cites Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization,

Reference 24

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Observation c6e01fb8-e605-432d-aa2b-85612ffa2af3 · outbound

This paper cites Adaptive Histogram Equalization and its Variations,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Adaptive Histogram Equalization and its Variations,

Reference 25

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Observation bb8dca2a-475b-4117-9d66-9b3658a32e36 · outbound

This paper cites Mixture Contrast Limited Adaptive Histogram Equalization for Underwater Image Enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Mixture Contrast Limited Adaptive Histogram Equalization for Underwater Image Enhancement,

Reference 26

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This paper cites Under- water Image Enhancement by Dehazing with Minimum Information Loss and Histogram Distribution Prior,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Under- water Image Enhancement by Dehazing with Minimum Information Loss and Histogram Distribution Prior,

Reference 27

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Observation 411a2d4d-20d3-4909-8c92-ff325149945e · outbound

This paper cites Polarimetric Image Recovery Method Combining Histogram Stretching for Underwater Imaging,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Polarimetric Image Recovery Method Combining Histogram Stretching for Underwater Imaging,

Reference 28

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Observation d3a043ac-401d-42bd-bb2a-5c4d0ac4deff · outbound

This paper cites Color balance and fusion for underwater image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Color balance and fusion for underwater image enhancement,

Reference 29

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Observation 3b5d3e71-9f8e-42e9-a5fa-1654ee2168a2 · outbound

This paper cites Underwater image enhancement based on color balance and multi-scale fusion,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater image enhancement based on color balance and multi-scale fusion,

Reference 30

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Observation f3fa2d05-c5db-4ac0-9169-998c965268f6 · outbound

This paper cites A new color correction method for underwater imaging,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement A new color correction method for underwater imaging,

Reference 31

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Observation fc904487-20a6-4a5a-8f56-79aab4d89ee4 · outbound

This paper cites Joint Iterative Color Correction and Dehazing for Underwater Image Enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Joint Iterative Color Correction and Dehazing for Underwater Image Enhancement,

Reference 32

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Observation a57f48bd-7fd5-42df-9df5-55e80ac3bb9d · outbound

This paper cites Multi-view underwater image enhancement method via embedded fusion mechanism,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Multi-view underwater image enhancement method via embedded fusion mechanism,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:41.082874Z

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Observation 260b8562-e6f7-41e1-9dc1-4e4085c444df · outbound

This paper cites Underwater image enhancement method via multi-feature prior fusion,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater image enhancement method via multi-feature prior fusion,

Reference 34

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Observation feb5f833-9738-441d-b85c-7e9b362581f0 · outbound

This paper cites Underwater image enhancement via weighted wavelet visual percep- tion fusion,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater image enhancement via weighted wavelet visual percep- tion fusion,

Reference 35

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Observation af448749-8d8a-4dbc-b686-9fd1200597fb · outbound

This paper cites Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement,

Reference 36

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Observation 50a93d45-c39a-42be-9883-59440e18b5e4 · outbound

This paper cites Underwater Image Enhancement via Minimal Color Loss and Lo- cally Adaptive Contrast Enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater Image Enhancement via Minimal Color Loss and Lo- cally Adaptive Contrast Enhancement,

Reference 37

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Observation 700838df-1b12-49fe-afc0-e1a576e84b23 · outbound

This paper cites Ca- gan: Class-condition attention gan for underwater image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Ca- gan: Class-condition attention gan for underwater image enhancement,

Reference 38

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Observation 63daaaf1-7bf2-4059-b260-bcbd37c01464 · outbound

This paper cites Pugan: Physical model-guided underwater image enhance- ment using gan with dual-discriminators,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Pugan: Physical model-guided underwater image enhance- ment using gan with dual-discriminators,

Reference 39

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Observation 202f45be-7429-4d1f-9759-d431ab8c2bf5 · outbound

This paper cites Uw-gan: Single-image depth estimation and image enhancement for underwater images,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Uw-gan: Single-image depth estimation and image enhancement for underwater images,

Reference 40

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Observation e0b5c9a4-1ff7-4811-a76a-2497c6f23273 · outbound

This paper cites Enhancing underwater imagery using generative adversarial networks,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Enhancing underwater imagery using generative adversarial networks,

Reference 41

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Observation 3ed761a6-6e78-4a72-b919-168e2644f056 · outbound

This paper cites An Underwater Image Enhancement Benchmark Dataset and Beyond,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement An Underwater Image Enhancement Benchmark Dataset and Beyond,

Reference 42

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Observation e3c44247-1960-4526-a4e4-0b19fa582fa0 · outbound

This paper cites Underwater image restoration via polymorphic large kernel cnns,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater image restoration via polymorphic large kernel cnns,

Reference 43

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Observation 7c70e170-b546-4672-98d1-4d24caaf6646 · outbound

This paper cites Cdf-uie: Leveraging cross-domain fusion for underwater image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Cdf-uie: Leveraging cross-domain fusion for underwater image enhancement,

Reference 44

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Observation fcd6a5f5-e02d-463d-9bf8-97dc641e0db4 · outbound

This paper cites Phaseformer: Phase-based attention mechanism for underwater image restoration and beyond,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Phaseformer: Phase-based attention mechanism for underwater image restoration and beyond,

Reference 45

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Observation 3b0e297b-0f0a-4ea3-817b-14917f2d7edc · outbound

This paper cites Towards progressive multi-frequency representation for image warping,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Towards progressive multi-frequency representation for image warping,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:39.534488Z

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Observation 2e290250-53ce-4ab9-b116-07fc905db96d · outbound

This paper cites A deep- shallow and global–local multi-feature fusion network for photometric stereo,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement A deep- shallow and global–local multi-feature fusion network for photometric stereo,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:39.358789Z

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Observation cf79e282-fac7-4a2d-942c-d4061c6e4df3 · outbound

This paper cites Pay attention to devils: A photometric stereo network for better details.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Pay attention to devils: A photometric stereo network for better details

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:39.150184Z

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Observation abc0d9c3-7c0e-4314-ad72-c81a57409760 · outbound

This paper cites Esti- mating high-resolution surface normals via low-resolution photometric stereo images,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Esti- mating high-resolution surface normals via low-resolution photometric stereo images,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:39.026262Z

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Observation 042e6fc1-30b9-49ea-8807-6d0205fe2ff3 · outbound

This paper cites Gr-psn: Learning to estimate surface normal and reconstruct photometric stereo images,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Gr-psn: Learning to estimate surface normal and reconstruct photometric stereo images,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:38.917502Z

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Observation 7e76ebae-78ed-4215-a975-7958bcad3977 · outbound

This paper cites Efficient inductive vision transformer for oriented object detection in remote sensing imagery,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Efficient inductive vision transformer for oriented object detection in remote sensing imagery,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:38.827723Z

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Observation 65d091b4-4002-4fa8-97d2-d126b27db5d1 · outbound

This paper cites Multi-scale weighted nuclear norm image restoration,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Multi-scale weighted nuclear norm image restoration,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:38.621175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 86d506b6-39e8-4cc5-8b90-f612237fbcd2 · outbound

This paper cites Multi-stage progressive image restoration,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Multi-stage progressive image restoration,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:38.383733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2a8ac9a3-3f26-462c-898d-09e79f5bfc6c · outbound

This paper cites Multi-scale single image dehazing using laplacian and gaussian pyramids,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Multi-scale single image dehazing using laplacian and gaussian pyramids,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:38.225830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6eb746ad-a4c0-4307-a358-5bc759a1121a · outbound

This paper cites Beyond gaussian pyramid: Multi-skip feature stacking for action recognition,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Beyond gaussian pyramid: Multi-skip feature stacking for action recognition,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:38.055042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 109a9b56-6aee-4457-823f-78912d5e12cc · outbound

This paper cites Iterative gaussian–laplacian pyramid network for hyperspectral image classification,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Iterative gaussian–laplacian pyramid network for hyperspectral image classification,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:37.906235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f3bdbfa5-6516-4868-8aa8-3369ef2fd89d · outbound

This paper cites The laplacian pyramid as a compact image code,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement The laplacian pyramid as a compact image code,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:37.784877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 294a0d70-1dc7-480a-8580-e60baa7d34b6 · outbound

This paper cites Local laplacian filters: Edge- aware image processing with a laplacian pyramid.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Local laplacian filters: Edge- aware image processing with a laplacian pyramid

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:37.639574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 55be2846-911b-4970-ae66-bc7c6b30d5f2 · outbound

This paper cites U-net: Convolutional net- works for biomedical image segmentation,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement U-net: Convolutional net- works for biomedical image segmentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:37.496550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b6e014f5-01f0-4a0e-96e6-433e3e79b456 · outbound

This paper cites Ucl-dehaze: toward real-world image dehazing via unsupervised contrastive learning,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Ucl-dehaze: toward real-world image dehazing via unsupervised contrastive learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:37.378270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c7646376-fe87-43f4-872b-a597f44cb4f5 · outbound

This paper cites Light-guided and cross-fusion u-net for anti-illumination image super-resolution,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Light-guided and cross-fusion u-net for anti-illumination image super-resolution,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:37.248456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 028aba15-9a3e-4fd2-8492-37846a0b93d0 · outbound

This paper cites Underwa- ter color restoration using u-net denoising autoencoder,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwa- ter color restoration using u-net denoising autoencoder,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:37.062787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d3e0c5ed-27ab-4894-9d5b-da98afc036ce · outbound

This paper cites Feature pyramid networks for object detection,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Feature pyramid networks for object detection,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:36.921384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d4b41cc7-3b8f-4389-96a7-5ce078759bf1 · outbound

This paper cites Parallel feature pyramid network for object detection,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Parallel feature pyramid network for object detection,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:36.765909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 38ce57f5-418c-4be8-bdc7-124d441ab1ad · outbound

This paper cites Pyramid attention network for image restoration,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Pyramid attention network for image restoration,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:36.605216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 56d6d972-5ea4-46c3-af34-05f3163daa2f · outbound

This paper cites Uw- former: Underwater image enhancement via a semi-supervised multi- scale transformer,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Uw- former: Underwater image enhancement via a semi-supervised multi- scale transformer,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:36.446660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a63747c3-180d-4038-bd54-1e71c3b7388d · outbound

This paper cites Sguie-net: Semantic attention guided underwater image enhancement with multi- scale perception,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Sguie-net: Semantic attention guided underwater image enhancement with multi- scale perception,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:27.802678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3090f15-efb8-404f-9171-254dc43c9c67 · outbound

This paper cites Hierarchical attention aggregation with multi-resolution feature learning for gan- based underwater image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Hierarchical attention aggregation with multi-resolution feature learning for gan- based underwater image enhancement,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:36.279395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8195cfdd-5159-4e4f-9d62-e5f1e64f198c · outbound

This paper cites Mffn: An underwater sensing scene im- age enhancement method based on multiscale feature fusion network,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Mffn: An underwater sensing scene im- age enhancement method based on multiscale feature fusion network,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:36.095094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 08c0f483-335a-4fb7-b75f-f30b92f3d513 · outbound

This paper cites Underwater image enhance- ment via extended multi-scale retinex,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Underwater image enhance- ment via extended multi-scale retinex,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:28.014811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98a4e9ca-17e5-43c7-b59f-81e0eafceefa · outbound

This paper cites Domain adaptation for underwater image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Domain adaptation for underwater image enhancement,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:35.918906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bb18cd4b-f1e2-4931-b3cd-e767876e7dd3 · outbound

This paper cites Edge-computing-enabled deep learning approach for low-light satellite image enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Edge-computing-enabled deep learning approach for low-light satellite image enhancement,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:35.708934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e8d89e0b-6228-463b-a683-1ce6194a2537 · outbound

This paper cites Remote sens- ing image super-resolution with residual split attention mechanism,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Remote sens- ing image super-resolution with residual split attention mechanism,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:35.554201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8e7b7808-ecc2-4f4e-b737-50a393ec1bf5 · outbound

This paper cites Act-sr:Aggregation connection transformer for remote sensing image super-resolution,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Act-sr:Aggregation connection transformer for remote sensing image super-resolution,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:35.403125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5cfb7dc0-8246-4af0-86c8-120568ff23e2 · outbound

This paper cites Hyda- net: A hybrid dense attention network for remote sensing multi-image super-resolution,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Hyda- net: A hybrid dense attention network for remote sensing multi-image super-resolution,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:35.221514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3b61171b-d197-4cfd-ab61-06cabfcdadc3 · outbound

This paper cites Atwo-branchmulti- scale residual attention network for single image super-resolution in remote sensing imagery,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Atwo-branchmulti- scale residual attention network for single image super-resolution in remote sensing imagery,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:35.040369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 87a75de1-037c-40d1-b3d9-9f4fab4f9160 · outbound

This paper cites Spectral– spatial generative adversarial network for super-resolution land cover mapping with multispectral remotely sensed imagery,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Spectral– spatial generative adversarial network for super-resolution land cover mapping with multispectral remotely sensed imagery,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:34.881266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b5025bb0-f06a-4cb7-99ab-50f6d8870448 · outbound

This paper cites A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:34.640172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8dec2e27-6695-463a-87d6-0e95aa7ccd76 · outbound

This paper cites an unresolved cited work.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-06T00:56:34.388497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7bbc7616-a120-47d1-bb13-2162f53156f9 · outbound

This paper cites Enhancing underwater images and videos by fusion,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Enhancing underwater images and videos by fusion,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:28.786633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f69f20bf-da08-432a-86de-2bd3c7dbafe2 · outbound

This paper cites Pdr-net: Perception-inspired single image dehazing network with refinement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Pdr-net: Perception-inspired single image dehazing network with refinement,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:34.210687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ef1f93d9-f78f-4ec4-96dc-bb449d201273 · outbound

This paper cites Contrastive semi- supervised learning for underwater image restoration via reliable bank,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Contrastive semi- supervised learning for underwater image restoration via reliable bank,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:33.971276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4303c977-fc85-4cb3-920f-664d232e72ba · outbound

This paper cites Diffwater: Underwater image enhancement based on conditional de- noising diffusion probabilistic model,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Diffwater: Underwater image enhancement based on conditional de- noising diffusion probabilistic model,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:33.731273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b7548b94-f4f6-413a-9644-863dd77012ce · outbound

This paper cites Ugif-net: An Efficient Fully Guided Information Flow Network for Underwater Image Enhancement,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Ugif-net: An Efficient Fully Guided Information Flow Network for Underwater Image Enhancement,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:33.553412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e01824c5-77cf-483e-9455-2c13e8a82fde · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:33.333743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 58aa87c7-609e-4ab6-b4eb-393479d6538e · outbound

This paper cites Highway Networks.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Highway Networks

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:29.325820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ee0d19a-5efe-4b07-aee3-522d82bdef9a · outbound

This paper cites Deep residual learning for image recognition,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Deep residual learning for image recognition,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:33.122964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e650a745-a4a0-4459-a638-1de981e18614 · outbound

This paper cites Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:29.489279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb213476-d62c-463b-8b15-bfdba0cd0c12 · outbound

This paper cites Selective kernel networks,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Selective kernel networks,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:32.949698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c3e71a45-5862-492e-84df-1701f58b4f62 · outbound

This paper cites Recurrent models of visualattention,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Recurrent models of visualattention,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:32.707712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 385b0ad2-c62f-4ed4-9604-3371e202fc6b · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Neural Machine Translation by Jointly Learning to Align and Translate

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:29.797655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65a24eef-b7f4-4609-b853-df7c7791d9c2 · outbound

This paper cites Show, attend and tell: Neural image caption generation with visual attention,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Show, attend and tell: Neural image caption generation with visual attention,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:32.526038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0d164396-a711-47ea-8bc5-7fe15af3d8d2 · outbound

This paper cites Attention is All You Need,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Attention is All You Need,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:30.086642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56d3c661-59c8-435f-b48a-57c9a4fa0e88 · outbound

This paper cites Generative adversarial nets,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Generative adversarial nets,

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:30.173027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddeadb3f-6d73-4859-94bc-6e46ab52fb6c · outbound

This paper cites Tcrn: A two-step underwater image enhancement network based on triple-color space feature recon- struction,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Tcrn: A two-step underwater image enhancement network based on triple-color space feature recon- struction,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:32.273161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b6c315c7-6574-493e-b0dc-98f319ca3bb5 · outbound

This paper cites Coc-ufgan: Underwater image enhancement based on color opponent compensation and dual-subnet underwater fusion generative adversarial network,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Coc-ufgan: Underwater image enhancement based on color opponent compensation and dual-subnet underwater fusion generative adversarial network,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:32.091412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e02e7993-b3d7-4861-981b-87fb3411606b · outbound

This paper cites Nas-fpn: Learning scalable feature pyramid architecture for object detection,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Nas-fpn: Learning scalable feature pyramid architecture for object detection,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:31.937613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 978763f0-5de7-45d5-9731-094dabe6a7f9 · outbound

This paper cites Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:30.546976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 336cadf5-b957-482e-ad4c-0b4eebad1ae7 · outbound

This paper cites A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:30.607327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 502fe3a4-3101-45ff-a309-c5e6cb834946 · outbound

This paper cites Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light,.

Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:56:31.720476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers