GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution | Paper Citation Record

Observation d833e9f5-11ba-4218-87c4-3cc035c19950 · outbound

This paper cites Enhancing satellite-based wildfire mon- itoring: Advanced contextual model using environmental and structural information,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Enhancing satellite-based wildfire mon- itoring: Advanced contextual model using environmental and structural information,

Reference 1

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This paper cites Machine learn- ing and remote sensing integration for leveraging urban sustainability: A review and framework,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Machine learn- ing and remote sensing integration for leveraging urban sustainability: A review and framework,

Reference 2

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This paper cites A uav-assisted edge framework for real-time disaster management,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution A uav-assisted edge framework for real-time disaster management,

Reference 3

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Observation 502842db-a349-4d9a-b654-e4d8f846b631 · outbound

This paper cites Survey of single image super-resolution reconstruction,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Survey of single image super-resolution reconstruction,

Reference 4

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Observation da97ec05-6619-4ba5-aa0c-1fc59bd23f0b · outbound

This paper cites Single image super-resolution reconstruction of enhanced loss function with multi-gpu train- ing,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Single image super-resolution reconstruction of enhanced loss function with multi-gpu train- ing,

Reference 5

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Observation 6bb12f17-a1df-4a14-a48b-03e5062ef03c · outbound

This paper cites Image super-resolution using deep convolutional networks,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Image super-resolution using deep convolutional networks,

Reference 6

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This paper cites Accurate image super-resolution using very deep convolutional networks,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Accurate image super-resolution using very deep convolutional networks,

Reference 7

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Observation 023064de-5638-4890-b2c5-effd581b1fad · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Photo-realistic single image super-resolution using a generative adversarial network,

Reference 8

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Observation 2253d6c9-2a0a-434f-ae9f-c0476637f730 · outbound

This paper cites Enhanced deep residual networks for single image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Enhanced deep residual networks for single image super-resolution,

Reference 9

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Observation cc713a12-2f72-4fb6-b94d-6c236e72bd64 · outbound

This paper cites Understanding the effective receptive field in deep convolutional neural networks,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Understanding the effective receptive field in deep convolutional neural networks,

Reference 10

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Observation f9e1505c-2c97-49c9-95f7-55b51057f3a1 · outbound

This paper cites Transformer-based multistage enhancement for remote sensing image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Transformer-based multistage enhancement for remote sensing image super-resolution,

Reference 11

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Observation a9909796-ff2f-422d-8f32-02d09c3d0ff4 · outbound

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

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Swinir: Image restoration using swin transformer,

Reference 12

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This paper cites Activating more pixels in image super-resolution transformer,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Activating more pixels in image super-resolution transformer,

Reference 13

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Observation 6380afd0-a31c-44a7-ae72-7f5494509b14 · outbound

This paper cites Stransfuse: Fusing swin transformer and convolutional neural network for remote sensing image semantic segmentation,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Stransfuse: Fusing swin transformer and convolutional neural network for remote sensing image semantic segmentation,

Reference 14

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Observation 986284e4-a969-4c75-ae75-f2932ae930a3 · outbound

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

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Mambair: A simple baseline for image restoration with state-space model,

Reference 15

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Observation d1c26c28-f05f-43b2-b5fd-3589a070b696 · outbound

This paper cites Frequency-assisted mamba for remote sensing image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Frequency-assisted mamba for remote sensing image super-resolution,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.710461Z

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Observation 831c2bf3-18eb-4a87-b5c0-dc2bf7ef0612 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 17

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Observation 9e80fe69-688a-4701-937f-e654d059cebd · outbound

This paper cites Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Reference 18

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Observation f2ae66a6-92a2-4f18-b4d6-db53ed640335 · outbound

This paper cites Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model

Reference 19

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Observation 6a1de6bc-3ace-4584-a5a2-8e3087c2154c · outbound

This paper cites Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV

Reference 20

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Observation c97a13d2-b8dd-4ba9-9ef2-8b03a5bc5404 · outbound

This paper cites How do vision transformers work?.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution How do vision transformers work?

Reference 21

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Observation 70913db6-068f-4fa1-8a78-d6d6212d0397 · outbound

This paper cites Super-resolution of single remote sensing image based on residual dense backprojection networks,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Super-resolution of single remote sensing image based on residual dense backprojection networks,

Reference 22

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Observation b037d4b5-e819-4686-9cfa-0366f1ce1ab0 · outbound

This paper cites Remote sensing image super-resolution using novel dense-sampling networks,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Remote sensing image super-resolution using novel dense-sampling networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.642986Z

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Observation 2384f1a2-19e7-4ce6-a336-2d3e8e02428c · outbound

This paper cites Hybrid-scale self-similarity exploitation for remote sensing image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Hybrid-scale self-similarity exploitation for remote sensing image super-resolution,

Reference 24

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This paper cites Hybrid attention- based u-shaped network for remote sensing image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Hybrid attention- based u-shaped network for remote sensing image super-resolution,

Reference 25

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Observation c6125c4f-02bd-4694-894d-8f1840364d09 · outbound

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GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Dual self- attention swin transformer for hyperspectral image super-resolution,

Reference 26

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Observation b2326f36-57ae-45d9-82c3-e66b3429048d · outbound

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GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Scale- aware backprojection transformer for single remote sensing image super- resolution,

Reference 27

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Observation 9d661fd4-6489-411f-8e5c-dfb7556f3399 · outbound

This paper cites Convformersr: Fusing transformers and convolutional neural networks for cross-sensor remote sensing imagery super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Convformersr: Fusing transformers and convolutional neural networks for cross-sensor remote sensing imagery super-resolution,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.541594Z

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Observation 4d5543dd-3009-4e5a-8808-80ce64e324c7 · outbound

This paper cites A dual-path feature reuse multi- scale network for remote sensing image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution A dual-path feature reuse multi- scale network for remote sensing image super-resolution,

Reference 29

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Observation 2f79f40f-c960-4f37-8ae6-0bc07d7d32cd · outbound

This paper cites Aeru-net: Adaptive edge recovery and attention u-shaped network for remote sensing image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Aeru-net: Adaptive edge recovery and attention u-shaped network for remote sensing image super-resolution,

Reference 30

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Observation 7eb4aeec-d4ca-43d2-9267-2c43d26c2820 · outbound

This paper cites Scat: Shift channel attention transformer for remote sensing image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Scat: Shift channel attention transformer for remote sensing image super-resolution,

Reference 31

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Observation 14f1b6a9-f838-472f-a261-99ff49edab13 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution RWKV: Reinventing RNNs for the Transformer Era

Reference 32

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This paper cites Vision-RWKV: Efficient and scalable visual per- ception with RWKV-like architectures,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Vision-RWKV: Efficient and scalable visual per- ception with RWKV-like architectures,

Reference 33

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Observation 3c3897b6-0c42-4e35-a7e8-51ff2196fa33 · outbound

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GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models

Reference 34

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Observation 4acadd3c-cf4c-4d2a-8017-39dc4a134517 · outbound

This paper cites Ten lectures on wavelets,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Ten lectures on wavelets,

Reference 35

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Observation cc0322f7-02f3-40f5-97bf-0673c358e6a8 · outbound

This paper cites Wavelet-srnet: A wavelet-based cnn for multi-scale face super resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Wavelet-srnet: A wavelet-based cnn for multi-scale face super resolution,

Reference 36

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Observation 11055450-2fc6-4610-9128-0ad9179e45bc · outbound

This paper cites Wavelet domain style transfer for an effective perception-distortion tradeoff in single image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Wavelet domain style transfer for an effective perception-distortion tradeoff in single image super-resolution,

Reference 37

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Observation 448faff4-e8be-4b3c-b4ad-88e3bf041b47 · outbound

This paper cites Wavelet-based dual recursive network for image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Wavelet-based dual recursive network for image super-resolution,

Reference 38

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GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Wavelet convo- lutions for large receptive fields,

Reference 39

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Observation 2b141fee-8126-4ed1-aeae-f6ee8cdcec65 · outbound

This paper cites Training generative image super-resolution models by wavelet-domain losses enables better control of artifacts,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Training generative image super-resolution models by wavelet-domain losses enables better control of artifacts,

Reference 40

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Observation cfd00303-5477-44b6-9ecf-b5ace7e94e0b · outbound

This paper cites Training transformer models by wavelet losses improves quantitative and visual performance in single image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Training transformer models by wavelet losses improves quantitative and visual performance in single image super-resolution,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.333084Z

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Observation 03b88802-91b7-494c-a4e2-d92b324892b4 · outbound

This paper cites Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network,

Reference 42

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Observation aedbd3b5-0820-487f-8b3b-109f83902332 · outbound

This paper cites Cbam: Convolutional block attention module,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Cbam: Convolutional block attention module,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T22:55:42.881194Z

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Observation 6efcb2e3-7cb6-4c62-b3e6-f31872e51085 · outbound

This paper cites Aid: A benchmark data set for performance evaluation of aerial scene classification,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Aid: A benchmark data set for performance evaluation of aerial scene classification,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T22:55:42.886727Z

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Observation 3585ab56-c411-4e3b-89ec-bec734cfdb64 · outbound

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

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Bag-of-visual-words and spatial extensions for land-use classification,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.275827Z

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Observation a759511e-1112-43d0-944c-b45624547b84 · outbound

This paper cites Image quality assess- ment: from error visibility to structural similarity,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Image quality assess- ment: from error visibility to structural similarity,

Reference 46

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Observation 06315daf-9807-4c83-9306-fdff46886b47 · outbound

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

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution The unreasonable effectiveness of deep features as a perceptual metric,

Reference 47

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Observation 107412fe-5f5f-46cd-af8d-8466a8a388eb · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Imagenet classification with deep convolutional neural networks,

Reference 48

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Observation 2692252a-acda-488f-aadb-045cd4b27621 · outbound

This paper cites Blind super-resolution with iterative kernel correction,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Blind super-resolution with iterative kernel correction,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.211582Z

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Observation b928f1c4-688b-461f-9efb-215f6c546611 · outbound

This paper cites Real-esrgan: Training real- world blind super-resolution with pure synthetic data,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Real-esrgan: Training real- world blind super-resolution with pure synthetic data,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T22:55:42.920766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2456a80a-e388-4288-8aa1-fda3c753259f · outbound

This paper cites Real-world remote sensing image super-resolution via a practical degradation model and a kernel-aware network,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Real-world remote sensing image super-resolution via a practical degradation model and a kernel-aware network,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.174952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c4893010-9c37-4f3d-9b6c-cb9cceb3151b · outbound

This paper cites Building bridges across spatial and temporal resolutions: Reference-based super-resolution via change priors and con- ditional diffusion model,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Building bridges across spatial and temporal resolutions: Reference-based super-resolution via change priors and con- ditional diffusion model,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.147944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fefdf34e-18d9-4ffd-bdec-0641aae158b1 · outbound

This paper cites Interpreting super-resolution networks with local attribution maps,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution Interpreting super-resolution networks with local attribution maps,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T22:55:42.938558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3aab5e4f-689d-4d47-a441-846728f8deb3 · outbound

This paper cites The 2018 pirm challenge on perceptual image super-resolution,.

GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution The 2018 pirm challenge on perceptual image super-resolution,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:55:43.111214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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