SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders | Paper Citation Record

Observation 320963f1-4300-43e5-9f26-af9bb85d00dc · outbound

This paper cites A Survey of Deep Learning Video Super- Resolution,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders A Survey of Deep Learning Video Super- Resolution,

Reference 1

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Observation b9c091a8-f7a3-4296-b561-87f5563bd2f4 · outbound

This paper cites Deep learning for efficient high- resolution image processing: A systematic review,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Deep learning for efficient high- resolution image processing: A systematic review,

Reference 2

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Observation af664f3a-ac5d-42c1-9a3f-3d31f77d21c2 · outbound

This paper cites Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems,

Reference 3

Resolution: verified exact; doi, observed 2026-08-03T22:28:39.345991Z

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Observation d0c7c424-70d9-4eb8-a0ab-c2a410335675 · outbound

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

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:49.002976Z

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Observation f1f7aa21-b3e0-48db-b7f7-93c9cf95632e · outbound

This paper cites A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:49.114388Z

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Observation 3327c58f-60e8-4dd3-84f3-74b90a07a523 · outbound

This paper cites Multi-level Wavelet-CNN for Image Restoration.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Multi-level Wavelet-CNN for Image Restoration

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-03T22:28:39.213789Z

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Observation f45e4c90-4e39-448c-8baf-fd1de9361388 · outbound

This paper cites Augmenting Perceptual Super-Resolution via Image Quality Predictors.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Augmenting Perceptual Super-Resolution via Image Quality Predictors

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:49.424746Z

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Observation 883334af-c9a9-4c81-bb17-612dd450a248 · outbound

This paper cites Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers,

Reference 8

Resolution: verified exact; doi, observed 2026-08-03T22:28:39.056395Z

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Observation 6b61936f-b0b7-4c91-83ef-91b6704f7830 · outbound

This paper cites Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:49.674756Z

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Observation ac0b4ae1-13e9-4a13-8bae-941eef9ba99c · outbound

This paper cites On the Spectral Bias of Neural Networks,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders On the Spectral Bias of Neural Networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:49.844745Z

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Observation e6ca0993-d3d1-4793-bc13-ac3b98f7c1aa · outbound

This paper cites Towards Understanding the Spectral Bias of Deep Learning.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Towards Understanding the Spectral Bias of Deep Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:49.955658Z

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Observation 75008284-9b65-4514-a7ed-7f3ff6c7022e · outbound

This paper cites Wavelet U-Net and the Chromatic Adaptation Transform for Single Image Dehazing,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Wavelet U-Net and the Chromatic Adaptation Transform for Single Image Dehazing,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:50.184748Z

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Observation 5d795133-e4a5-4376-a804-902bfaad78d5 · outbound

This paper cites Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:50.354931Z

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Observation fd6bcb01-ceb4-4543-9d31-d6d0adf1fd1a · outbound

This paper cites Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:50.474751Z

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Observation 17db67d8-0e1b-47d4-a358-624b1b32c6d2 · outbound

This paper cites WRANet: wavelet integrated residual attention U-Net network for medical image segmentation,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders WRANet: wavelet integrated residual attention U-Net network for medical image segmentation,

Reference 15

Resolution: malformed identifier; doi_truncated, observed 2026-08-03T22:28:38.924853Z

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Observation 6a768e40-e35f-4539-9340-3e6cbec48629 · outbound

This paper cites Wavelet U-Net++ for accurate lung nodule segmentation in CT scans: Improving early detection and diagnosis of lung cancer,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Wavelet U-Net++ for accurate lung nodule segmentation in CT scans: Improving early detection and diagnosis of lung cancer,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:50.754759Z

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Observation 6c285983-5e78-4b66-a85a-0f9e11efea00 · outbound

This paper cites Task-Driven Wavelets Using Constrained Empirical Risk Minimization,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Task-Driven Wavelets Using Constrained Empirical Risk Minimization,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:51.094749Z

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Observation 9f626c80-1d00-4594-8458-efc1d1712968 · outbound

This paper cites Focal Frequency Loss for Image Reconstruction and Synthesis,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Focal Frequency Loss for Image Reconstruction and Synthesis,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:51.153212Z

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Observation b263a4dc-70b3-462f-9216-0846156f05a5 · outbound

This paper cites Optimizing transformer-based network via advanced decoder design for medical image segmentation,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Optimizing transformer-based network via advanced decoder design for medical image segmentation,

Reference 19

Resolution: verified exact; doi, observed 2026-08-03T22:28:38.832406Z

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Observation 79ff7e3c-7c0c-44a8-ab68-e408d6ca85e3 · outbound

This paper cites The Role of Deep Learning in Medical Image Inpainting: A Systematic Review,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders The Role of Deep Learning in Medical Image Inpainting: A Systematic Review,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:51.421659Z

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Observation 97f1cf95-fef9-4b1d-b171-b704a6cb19e1 · outbound

This paper cites 3D medical image segmentation using the serial–parallel convolutional neural network and transformer based on cross‐ window self‐attention,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders 3D medical image segmentation using the serial–parallel convolutional neural network and transformer based on cross‐ window self‐attention,

Reference 21

Resolution: verified exact; doi, observed 2026-08-03T22:28:38.698179Z

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Observation 289179aa-29c7-4ffd-ba23-8775cefd29d8 · outbound

This paper cites On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:51.649226Z

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Observation dce014bd-a6f0-4a0a-a0af-adf857b6ea7e · outbound

This paper cites Deep learning in crack detection: A comprehensive scientometric review,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Deep learning in crack detection: A comprehensive scientometric review,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:51.724749Z

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Observation 5b7afd57-59a1-4244-8597-34b722ff9d02 · outbound

This paper cites Frequency-Aware Crack Segmentation Network (FACS-Net) for Thin-Cracks via Topology Preservation.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Frequency-Aware Crack Segmentation Network (FACS-Net) for Thin-Cracks via Topology Preservation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:51.826512Z

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Observation f5c1abc3-c08e-4f54-b282-83b7cfecce63 · outbound

This paper cites Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:52.024408Z

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Observation 7af577fb-655f-4c40-a290-5eb0b2947aaa · outbound

This paper cites Frequency‐aware denoising using a diffusion model for enhanced band‐limited and white noise removal in x‐ray acoustic computed tomography,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Frequency‐aware denoising using a diffusion model for enhanced band‐limited and white noise removal in x‐ray acoustic computed tomography,

Reference 26

Resolution: verified exact; doi, observed 2026-08-03T22:28:38.482729Z

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

Observation 29f853df-6851-4fc3-9d68-f0fd64d7d047 · outbound

This paper cites Exploring a Frequency- Domain Attention-Guided Cascade U-Net: Towards Spatially Tunable Segmentation of Vasculature,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Exploring a Frequency- Domain Attention-Guided Cascade U-Net: Towards Spatially Tunable Segmentation of Vasculature,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:52.240602Z

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Observation 003d3c2b-9e48-462f-a4e5-417166aed00c · outbound

This paper cites Frequency-Aware Crack Segmentation Network (Facs-Net) for Thin- Cracks Via Topology Preservation,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Frequency-Aware Crack Segmentation Network (Facs-Net) for Thin- Cracks Via Topology Preservation,

Reference 28

Resolution: verified exact; doi, observed 2026-08-03T22:28:38.320915Z

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Observation df70ea8d-417b-4ab5-a2d4-d75401dd1ffc · outbound

This paper cites CascadedGaze: Efficiency in Global Context Extraction for Image Restoration.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders CascadedGaze: Efficiency in Global Context Extraction for Image Restoration

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:52.366822Z

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Observation 927a8640-b30e-441b-b8ca-c0fe530455f4 · outbound

This paper cites Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Assessing the Image Quality of Digitally Reconstructed Radiographs from Chest CT,

Reference 30

Resolution: verified exact; doi, observed 2026-08-03T22:28:38.136186Z

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

Observation a184d708-59f7-47de-b243-0539ceae22fd · outbound

This paper cites On Removing Interpolation and Resampling Artifacts in Rigid Image Registration,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders On Removing Interpolation and Resampling Artifacts in Rigid Image Registration,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:52.501110Z

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Observation 6a1d9687-98c6-4dde-9101-b714f52847ce · outbound

This paper cites How Convolutional Neural Networks Deal with Aliasing.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders How Convolutional Neural Networks Deal with Aliasing

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-03T22:28:37.848044Z

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

Observation 26103e81-9f36-4dfd-9391-b3b48b78b4d3 · outbound

This paper cites Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:52.670835Z

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Observation 74323fc6-2128-4b61-bf89-6a3c744e4bf6 · outbound

This paper cites An Effective UNet Using Feature Interaction and Fusion for Organ Segmentation in Medical Image.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders An Effective UNet Using Feature Interaction and Fusion for Organ Segmentation in Medical Image

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-03T22:28:37.557460Z

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

Observation f018616b-62c1-4430-a152-2fcc546bfda3 · outbound

This paper cites LMSC-UNet: A Lightweight U-Net with Modified Skip Connections for Semantic Segmentation:,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders LMSC-UNet: A Lightweight U-Net with Modified Skip Connections for Semantic Segmentation:,

Reference 35

Resolution: verified exact; doi, observed 2026-08-03T22:28:37.235829Z

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Observation e40a957b-b812-4668-87f4-cd20ca131702 · outbound

This paper cites WST: Wavelet- Based Multi-scale Tuning for Visual Transfer Learning.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders WST: Wavelet- Based Multi-scale Tuning for Visual Transfer Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.039096Z

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Observation b960b985-646b-42a2-a491-ae4b29f6e146 · outbound

This paper cites Towards Building More Robust Models with Frequency Bias,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Towards Building More Robust Models with Frequency Bias,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.094856Z

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Observation cdcbc973-1b5d-4302-9ac3-5ef9e67b1020 · outbound

This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.174752Z

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Observation d024ad73-415a-4ed2-9490-81d0be43c194 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Fourier Neural Operator for Parametric Partial Differential Equations

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.274768Z

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Observation d075d569-c29a-48ae-969a-417c9770755e · outbound

This paper cites Implicit Neural Representations with Periodic Activation Functions,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Implicit Neural Representations with Periodic Activation Functions,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.313873Z

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Observation 7c32f7e5-337f-458c-bd5e-1bd4b4e43fe4 · outbound

This paper cites CBAM: Convolutional Block Attention Module,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders CBAM: Convolutional Block Attention Module,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.380279Z

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Observation 35822cc5-75ab-49e9-a60a-8f965b6ddcb6 · outbound

This paper cites Model-based Analysis of ChIP- Seq (MACS),.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Model-based Analysis of ChIP- Seq (MACS),

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.467476Z

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Observation 80dd0d8b-5b6b-4f29-9253-2fbbe6a40d9f · outbound

This paper cites Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.536447Z

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Observation 64525842-6e51-42f4-aa10-c9d6e1de1306 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Deep Residual Learning for Image Recognition,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.587341Z

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Observation 4e23cae0-b275-4ed3-85d8-96d20d387404 · outbound

This paper cites SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.653765Z

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Observation cc4d4043-2030-4b7f-8962-13228e131afd · outbound

This paper cites Evaluating Road Crack Segmentation Performance in Participatory Sensing: An Exploration of Alternative Metrics,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Evaluating Road Crack Segmentation Performance in Participatory Sensing: An Exploration of Alternative Metrics,

Reference 46

Resolution: verified exact; doi, observed 2026-08-03T22:28:37.040379Z

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

Observation 37594070-620b-4236-b26d-0e6134db7853 · outbound

This paper cites A High- Quality Denoising Dataset for Smartphone Cameras,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders A High- Quality Denoising Dataset for Smartphone Cameras,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.801958Z

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Observation 4fa2034a-f775-4ddc-aa69-0f0fdc0d880d · outbound

This paper cites NTIRE 2019 Challenge on Real Image Denoising: Methods and Results,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders NTIRE 2019 Challenge on Real Image Denoising: Methods and Results,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:53.839488Z

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Observation cc2d351d-4bbe-4d11-8866-df57d57f369c · outbound

This paper cites Benchmarking Denoising Algorithms with Real Photographs,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Benchmarking Denoising Algorithms with Real Photographs,

Reference 49

Resolution: verified exact; doi, observed 2026-08-03T22:28:36.891160Z

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

Observation 469bef94-6c9f-488c-81d6-baf14d940d0a · outbound

This paper cites Learning Enriched Features for Real Image Restoration and Enhancement,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Learning Enriched Features for Real Image Restoration and Enhancement,

Reference 50

Resolution: malformed identifier; doi_truncated, observed 2026-08-03T22:28:36.619456Z

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Observation f543c22d-8bed-4d80-af45-45a430383a7c · outbound

This paper cites Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-03T22:28:36.457407Z

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

Observation d0987504-1ded-4788-8bd4-8337eb078645 · outbound

This paper cites Simple Baselines for Image Restoration,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Simple Baselines for Image Restoration,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:54.084810Z

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Observation b6f560aa-0c3f-4208-b953-1e61112caf68 · outbound

This paper cites A lightweight semantic segmentation method for concrete bridge surface diseases based on improved DeeplabV3+,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders A lightweight semantic segmentation method for concrete bridge surface diseases based on improved DeeplabV3+,

Reference 53

Resolution: verified exact; doi, observed 2026-08-03T22:28:36.230417Z

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Observation b12ffb21-e8a9-4a56-9ca8-df4941de7df1 · outbound

This paper cites A dual encoder crack segmentation network with Haar wavelet-based high–low frequency attention,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders A dual encoder crack segmentation network with Haar wavelet-based high–low frequency attention,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:54.230859Z

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Observation ec73edea-4b04-4ec8-a23c-5d9dcf1802d8 · outbound

This paper cites Cross Aggregation Transformer for Image Restoration.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Cross Aggregation Transformer for Image Restoration

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:54.361828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f806d77-bd0a-4dda-ac03-5b40166399e4 · outbound

This paper cites MAXIM: Multi-Axis MLP for Image Processing,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders MAXIM: Multi-Axis MLP for Image Processing,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:54.426171Z

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Unavailable: canonical work link unavailable.

Observation 88decd51-4988-427b-a5da-601efe84d2e9 · outbound

This paper cites HINet: Half Instance Normalization Network for Image Restoration,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders HINet: Half Instance Normalization Network for Image Restoration,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:54.532702Z

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Unavailable: canonical work link unavailable.

Observation 571ec6d3-ce31-452f-8f07-0fff3d4eb9b2 · outbound

This paper cites CycleISP: Real Image Restoration via Improved Data Synthesis,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders CycleISP: Real Image Restoration via Improved Data Synthesis,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:54.637004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 253b6983-68d5-4b84-921b-6c2c510b495d · outbound

This paper cites Multi-Stage Progressive Image Restoration,.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Multi-Stage Progressive Image Restoration,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:54.729747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e13eeca-f912-4287-b2ef-2cd2e391da9e · outbound

This paper cites an unresolved cited work.

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders Unresolved cited work

Reference 2740

Resolution: unresolved; no resolver link, observed 2026-08-03T22:23:50.264733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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