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Double-Layer Search and Adaptive Pooling Fusion for Reference-Based Image Super-Resolution

https://doi.org/10.1145/3604937
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_18openalex-snapshot
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
inferredcites →https://doi.org/10.1007/978-3-030-58539-6_12openalex-snapshot
Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring
inferredcites →https://doi.org/10.1007/978-3-319-10593-2_13openalex-snapshot
Learning a Deep Convolutional Network for Image Super-Resolution
inferredcites →https://doi.org/10.1007/978-3-642-27413-8_47openalex-snapshot
On Single Image Scale-Up Using Sparse-Representations
inferredcites →https://doi.org/10.1007/s11042-016-4020-zopenalex-snapshot
Sketch-based manga retrieval using manga109 dataset
inferredcites →https://doi.org/10.1016/j.patcog.2020.107332openalex-snapshot
Understanding adversarial attacks on deep learning based medical image analysis systems
inferredcites →https://doi.org/10.1080/2150704x.2019.1693071openalex-snapshot
DeepSat V2: feature augmented convolutional neural nets for satellite image classification
inferredcites →https://doi.org/10.1109/83.951537openalex-snapshot
New edge-directed interpolation
inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7299156openalex-snapshot
Single image super-resolution from transformed self-exemplars
inferredcites →https://doi.org/10.1109/cvpr.2016.207openalex-snapshot
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
inferredcites →https://doi.org/10.1109/cvpr.2016.265openalex-snapshot
Image Style Transfer Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.19openalex-snapshot
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00262openalex-snapshot
Residual Dense Network for Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00545openalex-snapshot
Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvprw.2017.151openalex-snapshot
Enhanced Deep Residual Networks for Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/iccv.2017.481openalex-snapshot
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
inferredcites →https://doi.org/10.1109/icpr.2010.579openalex-snapshot
Image Quality Metrics: PSNR vs. SSIM
inferredcites →https://doi.org/10.1109/msp.2003.1203207openalex-snapshot
Super-resolution image reconstruction: a technical overview
inferredcites →https://doi.org/10.1109/tci.2018.2838457openalex-snapshot
Cross-Scale Reference-Based Light Field Super-Resolution
inferredcites →https://doi.org/10.1109/tcsvt.2021.3078436openalex-snapshot
Lightweight Image Super-Resolution With Expectation-Maximization Attention Mechanism
inferredcites →https://doi.org/10.1109/tip.2005.851684openalex-snapshot
Image up-sampling using total-variation regularization with a new observation model
inferredcites →https://doi.org/10.1109/tip.2006.877407openalex-snapshot
An edge-guided image interpolation algorithm via directional filtering and data fusion
inferredcites →https://doi.org/10.1109/tip.2010.2095871openalex-snapshot
Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement
inferredcites →https://doi.org/10.1109/tip.2013.2279315openalex-snapshot
Landmark Image Super-Resolution by Retrieving Web Images
inferredcites →https://doi.org/10.1109/tip.2015.2414877openalex-snapshot
Single Image Superresolution Based on Gradient Profile Sharpness
inferredcites →https://doi.org/10.1109/tip.2017.2750403openalex-snapshot
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
inferredcites →https://doi.org/10.1109/tip.2019.2916751openalex-snapshot
AttGAN: Facial Attribute Editing by Only Changing What You Want
inferredcites →https://doi.org/10.1109/tmm.2021.3092571openalex-snapshot
Cross View Capture for Stereo Image Super-Resolution
inferredcites →https://doi.org/10.1109/tpami.2015.2439281openalex-snapshot
Image Super-Resolution Using Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/tsmc.1973.4309314openalex-snapshot
Textural Features for Image Classification
inferredcites →https://doi.org/10.1145/1275808.1276496openalex-snapshot
Image upsampling via imposed edge statistics
inferredcites →https://doi.org/10.1145/3282445openalex-snapshot
Symmetrical Residual Connections for Single Image Super-Resolution
inferredcites →https://doi.org/10.1145/3377874openalex-snapshot
A Deep Learning Approach for Face Hallucination Guided by Facial Boundary Responses
inferredcites →https://doi.org/10.1145/3398685openalex-snapshot
Kernel Attention Network for Single Image Super-Resolution
inferredcites →https://doi.org/10.1145/3417333openalex-snapshot
Multi-task Learning-based All-in-one Collaboration Framework for Degraded Image Super-resolution
inferredcites →https://doi.org/10.1145/3465220openalex-snapshot
Medical Image Classification based on an Adaptive Size Deep Learning Model
inferredcites →https://doi.org/10.48550/arxiv.1406.2661openalex-snapshot
Generative Adversarial Networks
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1505.07376openalex-snapshot
Texture Synthesis Using Convolutional Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4824802openalex-extracted
Hstr-Net: Reference Based Video Super-Resolution for Aerial Surveillance with Dual Cameras
inferred← citeshttps://doi.org/10.1007/978-3-031-96255-4_20openalex-snapshot
Extending Reference-Based Texture Transformers for Image Dehazing
inferred← citeshttps://doi.org/10.1007/s00138-025-01685-wopenalex-snapshot
Hstr-net: reference based video super-resolution with dual cameras
inferred← citeshttps://doi.org/10.1145/3656048openalex-snapshot
Multi-Domain Image-to-Image Translation with Cross-Granularity Contrastive Learning
inferred← citeshttps://doi.org/10.1371/journal.pone.0333398openalex-snapshot
Image super-resolution reconstruction algorithm based on multi-scale recursive attention and feature fusion
inferred← citeshttps://doi.org/10.2139/ssrn.5347487openalex-snapshot
Extracter: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution
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