DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution | Paper Citation Record

Observation 08b3e736-577a-4563-8065-acb1ac3017ba · outbound

This paper cites Ntire 2017 challenge on single image super- resolution: Dataset and study.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Ntire 2017 challenge on single image super- resolution: Dataset and study

Reference 1

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Observation 1e772557-27c6-475b-8af4-76ebcf130095 · outbound

This paper cites Fast, accurate, and lightweight super-resolution with cascading residual network.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Fast, accurate, and lightweight super-resolution with cascading residual network

Reference 2

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Observation 042eb658-2f7d-4d97-9893-f39a79652989 · outbound

This paper cites Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Em- bedding.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Em- bedding

Reference 3

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Observation dfdf9ba9-3bc3-4858-aa37-722fb4825d23 · outbound

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

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Activating more pixels in image super-resolution transformer

Reference 4

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Observation 9bfa74c5-b7c2-4680-a5f3-22d5a5febb9b · outbound

This paper cites Dual aggregation transformer for image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Dual aggregation transformer for image super-resolution

Reference 5

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Observation 6c840122-bb6e-46f8-a9f3-28f2e599a105 · outbound

This paper cites Recursive generalization transformer for image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Recursive generalization transformer for image super-resolution

Reference 6

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Observation 61c29c78-7a23-4ddd-9e6e-496afc1a35e3 · outbound

This paper cites Image super-resolution using deep convolutional networks.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 38(2):295–307, 2016.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Image super-resolution using deep convolutional networks.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 38(2):295–307, 2016

Reference 7

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Observation 52ef343e-b5a5-4717-a83b-56eab392bc4e · outbound

This paper cites Accelerating the super-resolution convolutional neural network.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Accelerating the super-resolution convolutional neural network

Reference 8

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Observation 5c8c4c0d-112d-4e4d-96b7-050426a29d77 · outbound

This paper cites Fast and memory-efficient network towards efficient image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Fast and memory-efficient network towards efficient image super-resolution

Reference 9

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Observation 10670126-7ed4-40c7-ba6a-78e7f3a83006 · outbound

This paper cites Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural Networks, 107:3–11, 2018.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural Networks, 107:3–11, 2018

Reference 10

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Observation 98f6c61a-2d7b-48bb-93da-fbcbfb59759c · outbound

This paper cites Fourier space losses for efficient per- ceptual image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Fourier space losses for efficient per- ceptual image super-resolution

Reference 11

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Observation 1ae8166e-b52e-455a-8ef4-9a62b6dc6fef · outbound

This paper cites Special issue on deep reinforcement learning.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Special issue on deep reinforcement learning

Reference 12

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Observation 51528915-b8e0-483b-b396-350168eea555 · outbound

This paper cites Drct: Saving image super- resolution away from information bottleneck.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Drct: Saving image super- resolution away from information bottleneck

Reference 13

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Observation 525b9cb7-c843-4966-828a-587c150208bc · outbound

This paper cites Feature distillation interaction weighting network for lightweight image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Feature distillation interaction weighting network for lightweight image super-resolution

Reference 14

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Observation 1951a0f6-f2ba-47e0-9387-44c7e2c7591d · outbound

This paper cites Lightweight image super- resolution with information multi-distillation network.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Lightweight image super- resolution with information multi-distillation network

Reference 15

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Observation 80af0681-2c93-412a-bfb6-4671b8ef8537 · outbound

This paper cites Single image super-resolution from transformed self-exemplars.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Single image super-resolution from transformed self-exemplars

Reference 16

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Observation 30fbb233-c925-4a89-9588-4e6afb3a38cb · outbound

This paper cites Kingma and Jimmy Ba.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Kingma and Jimmy Ba

Reference 17

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Observation 70d78ebe-ad42-4a95-9784-c468deb2edf1 · outbound

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

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Accurate image super-resolution using very deep convolutional networks

Reference 18

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Observation 4d46c334-b9ce-45cf-be4b-552681732f1c · outbound

This paper cites Murat Tekalp, and Zafer Dogan.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Murat Tekalp, and Zafer Dogan

Reference 19

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Observation bd0e469e-69cf-4db3-afcf-d020d7178bc8 · outbound

This paper cites La- par: Linearly-assembled pixel-adaptive regression network for single image super- resolution and beyond.Advances in Neural Information Processing Systems, 33, 2020.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution La- par: Linearly-assembled pixel-adaptive regression network for single image super- resolution and beyond.Advances in Neural Information Processing Systems, 33, 2020

Reference 21

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Observation 29829819-aaaf-4a7b-9a3c-271277937599 · outbound

This paper cites Deep lapla- cian pyramid networks for fast and accurate super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Deep lapla- cian pyramid networks for fast and accurate super-resolution

Reference 22

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Observation 6254a77b-2909-4be2-a7f6-047fa15236b8 · outbound

This paper cites Swinir: Image restoration using swin transformer.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Swinir: Image restoration using swin transformer

Reference 23

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Observation 5b6b6556-a8b1-4ecb-8d3c-563f9689b99f · outbound

This paper cites Blueprint separable residual network for efficient image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Blueprint separable residual network for efficient image super-resolution

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:42:29.686106Z

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Observation 234d191d-1d56-4917-a1dd-9a55dfef0804 · outbound

This paper cites SGDR: Stochastic gradient descent with warm restarts.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution SGDR: Stochastic gradient descent with warm restarts

Reference 25

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Observation d1f4c03d-1316-4240-92a6-0f7f0a08db26 · outbound

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

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Enhanced deep residual networks for single image super-resolution

Reference 26

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Observation 43f60421-c70c-4bea-8386-86e3f4c281b7 · outbound

This paper cites Multi-attention based ultra lightweight image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Multi-attention based ultra lightweight image super-resolution

Reference 27

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Observation 347c5cfb-6840-4760-aeaa-877335e2cb52 · outbound

This paper cites Martin, C.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Martin, C

Reference 28

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This paper cites Courville.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Courville

Reference 29

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This paper cites Single image super-resolution via a holistic attention network.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Single image super-resolution via a holistic attention network

Reference 30

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Observation c4bef311-3fca-4d8e-a15b-d1c209a03621 · outbound

This paper cites ShuffleMixer: An efficient convnet for image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution ShuffleMixer: An efficient convnet for image super-resolution

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:34.508729Z

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Observation 6169271b-7210-47cb-9d9a-548e8878c47d · outbound

This paper cites Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:34.704368Z

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Observation 476ff430-aebf-465e-9f7a-7f5e2df80fe6 · outbound

This paper cites Lightweight image super-resolution with enhanced cnn.Knowledge- Based Systems, page 106235, 2020.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Lightweight image super-resolution with enhanced cnn.Knowledge- Based Systems, page 106235, 2020

Reference 33

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Observation 7c30db2f-e0d1-4a29-afe6-b0c1a2f8143b · outbound

This paper cites Spatially-adaptive feature modulation for efficient image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Spatially-adaptive feature modulation for efficient image super-resolution

Reference 34

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Observation 7efed538-e5ae-4c62-b250-1d3ece3d7dc5 · outbound

This paper cites Exploring sparsity in image super-resolution for efficient inference.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Exploring sparsity in image super-resolution for efficient inference

Reference 35

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Observation 8b965fb3-9e31-4f8e-a812-1bf36941862c · outbound

This paper cites Swift parameter-free attention network for efficient super- resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Swift parameter-free attention network for efficient super- resolution

Reference 36

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Observation 0994b18e-eb50-428d-9ed8-87c7306d500d · outbound

This paper cites Bovik, H.R.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Bovik, H.R

Reference 37

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Observation 6535de40-e937-48c4-bf03-1ae2004f3db0 · outbound

This paper cites The super-resolution recon- struction algorithm of multi-scale dilated convolution residual network.Fron- tiers in Neurorobotics, V olume 18, 2024.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution The super-resolution recon- struction algorithm of multi-scale dilated convolution residual network.Fron- tiers in Neurorobotics, V olume 18, 2024

Reference 38

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Observation 7fde4077-ce1e-4686-a71b-59c64f116bb4 · outbound

This paper cites On single image scale-up using sparse-representations.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution On single image scale-up using sparse-representations

Reference 39

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Observation 6f202f39-0120-4c8d-94fb-d5531efe7572 · outbound

This paper cites See more details: Efficient image super-resolution by experts mining.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution See more details: Efficient image super-resolution by experts mining

Reference 40

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Observation f7cc0ce2-110d-4859-8365-c2d296c00209 · outbound

This paper cites Edge-oriented convolution block for real- time super resolution on mobile devices.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Edge-oriented convolution block for real- time super resolution on mobile devices

Reference 41

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Observation f9d29f16-208b-4475-9f59-50c2a765ee0e · outbound

This paper cites Efficient long-range attention network for image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Efficient long-range attention network for image super-resolution

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:33.359926Z

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Observation 9c980f0c-7d7c-41a9-abe2-56a66d738592 · outbound

This paper cites SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution

Reference 43

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Observation f4c0eb1b-e130-4a40-811b-36d515636b34 · outbound

This paper cites Residual dense network for image super-resolution.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Residual dense network for image super-resolution

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:33.291029Z

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Observation 9e62901e-a450-4fa9-b471-b1e1d1ff4593 · outbound

This paper cites Efficient image super-resolution using pixel attention.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Efficient image super-resolution using pixel attention

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:33.213830Z

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Observation 78b15441-5ac4-42bc-9d57-d95d6edce749 · outbound

This paper cites Image super-resolution using very deep residual channel attention networks.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution Image super-resolution using very deep residual channel attention networks

Reference 46

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This paper cites ISBN 978-3-642-27413-8.

DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution ISBN 978-3-642-27413-8

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:42:33.631827Z

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