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Adapting Single-Image Super-Resolution Models to Video Super-Resolution: A Plug-and-Play Approach

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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/10.1007/978-3-031-19815-1_37openalex-snapshot

XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

inferredcites →https://doi.org/10.1007/978-3-031-25063-7_37openalex-snapshot

Sliding Window Recurrent Network for Efficient Video Super-Resolution

inferredcites →https://doi.org/10.1007/s00371-023-02795-0openalex-snapshot

MFFN: image super-resolution via multi-level features fusion network

inferredcites →https://doi.org/10.1007/s10462-022-10147-yopenalex-snapshot

Video super-resolution based on deep learning: a comprehensive survey

inferredcites →https://doi.org/10.1007/s11063-021-10593-9openalex-snapshot

Video Super-Resolution with Frame-Wise Dynamic Fusion and Self-Calibrated Deformable Alignment

inferredcites →https://doi.org/10.1007/s11263-018-01144-2openalex-snapshot

Video Enhancement with Task-Oriented Flow

inferredcites →https://doi.org/10.1016/j.jvcir.2023.103776openalex-snapshot

FFTI: Image inpainting algorithm via features fusion and two-steps inpainting

inferredcites →https://doi.org/10.1016/j.patcog.2020.107619openalex-snapshot

Video super-resolution based on a spatio-temporal matching network

inferredcites →https://doi.org/10.1016/j.surfin.2022.101772openalex-snapshot

Molecular beam epitaxy growth of high mobility InN film for high-performance broadband heterointerface photodetectors

inferredcites →https://doi.org/10.1038/s41586-021-03819-2openalex-snapshot

Highly accurate protein structure prediction with AlphaFold

inferredcites →https://doi.org/10.1109/access.2021.3098326openalex-snapshot

Efficient Video Super-Resolution via Hierarchical Temporal Residual Networks

inferredcites →https://doi.org/10.1109/asap52443.2021.00019openalex-snapshot

Real-Time Super-Resolution System of 4K-Video Based on Deep Learning

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.2016.90openalex-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.2018.00179openalex-snapshot

Deep Back-Projection Networks for Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr.2018.00262openalex-snapshot

Residual Dense Network for Image Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr.2018.00340openalex-snapshot

Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation

inferredcites →https://doi.org/10.1109/cvpr.2018.00693openalex-snapshot

Frame-Recurrent Video Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr.2019.00402openalex-snapshot

Recurrent Back-Projection Network for Video Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00342openalex-snapshot

TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00803openalex-snapshot

Video Super-Resolution With Temporal Group Attention

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00491openalex-snapshot

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01330openalex-snapshot

QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

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/cvprw.2019.00184openalex-snapshot

The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

inferredcites →https://doi.org/10.1109/cvprw.2019.00247openalex-snapshot

EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

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/iccv48922.2021.00986openalex-snapshot

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

inferredcites →https://doi.org/10.1109/iccvw54120.2021.00210openalex-snapshot

SwinIR: Image Restoration Using Swin Transformer

inferredcites →https://doi.org/10.1109/icip.2019.8803297openalex-snapshot

Video Super-Resolution Based on 3D-CNNS with Consideration of Scene Change

inferredcites →https://doi.org/10.1109/icpr.2010.579openalex-snapshot

Image Quality Metrics: PSNR vs. SSIM

inferredcites →https://doi.org/10.1109/icpr48806.2021.9412623openalex-snapshot

Wavelet Attention Embedding Networks for Video Super-Resolution

inferredcites →https://doi.org/10.1109/jas.2021.1004009openalex-snapshot

Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network

inferredcites →https://doi.org/10.1109/lsp.2020.3013518openalex-snapshot

Deformable 3D Convolution for Video Super-Resolution

inferredcites →https://doi.org/10.1109/tcyb.2019.2952710openalex-snapshot

Cascading and Enhanced Residual Networks for Accurate Single-Image Super-Resolution

inferredcites →https://doi.org/10.1109/tcyb.2020.2970104openalex-snapshot

MADNet: A Fast and Lightweight Network for Single-Image Super Resolution

inferredcites →https://doi.org/10.1109/tip.2003.819861openalex-snapshot

Image quality assessment: from error visibility to structural similarity

inferredcites →https://doi.org/10.1109/tip.2020.2967596openalex-snapshot

Deep Video Super-Resolution Using HR Optical Flow Estimation

inferredcites →https://doi.org/10.1109/tip.2022.3146625openalex-snapshot

Video Super-Resolution via a Spatio-Temporal Alignment Network

inferredcites →https://doi.org/10.1109/tpami.2013.127openalex-snapshot

On Bayesian Adaptive Video Super Resolution

inferredcites →https://doi.org/10.1109/tpami.2015.2439281openalex-snapshot

Image Super-Resolution Using Deep Convolutional Networks

inferredcites →https://doi.org/10.1145/3390462openalex-snapshot

A Deep Journey into Super-resolution

inferredcites →https://doi.org/10.3390/s22218476openalex-snapshot

Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network

inferredcites →https://doi.org/10.3390/s22249604openalex-snapshot

A Local and Non-Local Features Based Feedback Network on Super-Resolution

inferredcites →https://doi.org/10.3390/s23031248openalex-snapshot

LRF-SRNet: Large-Scale Super-Resolution Network for Estimating Aircraft Pose on the Airport Surface

inferredcites →https://doi.org/10.3390/s23052439openalex-snapshot

Detection of Occluded Small Commodities Based on Feature Enhancement under Super-Resolution

inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot

Exploiting Generative AI to Scale up Intelligent Tutoring Systems

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.52202/068431-0028openalex-snapshot

Recurrent Video Restoration Transformer with Guided Deformable Attention

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.4679125openalex-extracted

inferred← citeshttps://doi.org/10.1007/s00371-025-04198-9openalex-snapshot

Enhanced optical flow estimation via multiscale kernel selection and super-resolution integration

inferred← citeshttps://doi.org/10.1016/j.image.2024.117175openalex-snapshot

A ‘deep’ review of video super-resolution

inferred← citeshttps://doi.org/10.3390/s24154819openalex-snapshot

Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes

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