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Fast Spatio-Temporal Residual Network for Video Super-Resolution

https://doi.org/10.1109/cvpr.2019.01077

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What this work says

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

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Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

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Learning a Deep Convolutional Network for Image Super-Resolution

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Accelerating the Super-Resolution Convolutional Neural Network

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Gradient-based learning applied to document recognition

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Going deeper with convolutions

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Deeply-Recursive Convolutional Network for Image Super-Resolution

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Accurate Image Super-Resolution Using Very Deep Convolutional Networks

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Seven Ways to Improve Example-Based Single Image Super Resolution

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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

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Deep Residual Learning for Image Recognition

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation

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

Residual Dense Network for Image Super-Resolution

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

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A Closer Look at Spatiotemporal Convolutions for Action Recognition

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Frame-Recurrent Video Super-Resolution

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Enhanced Deep Residual Networks for Single Image Super-Resolution

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Anchored Neighborhood Regression for Fast Example-Based Super-Resolution

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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

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Image Super-Resolution Using Dense Skip Connections

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Video Super-Resolution With Convolutional Neural Networks

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Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

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Super-Resolution Without Explicit Subpixel Motion Estimation

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Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

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Learning Temporal Dynamics for Video Super-Resolution: A Deep Learning Approach

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Why ResNet Works? Residuals Generalize

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3D Convolutional Neural Networks for Human Action Recognition

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On Bayesian Adaptive Video Super Resolution

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Image Super-Resolution Using Deep Convolutional Networks

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Video Super-Resolution via Bidirectional Recurrent Convolutional Networks

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ImageNet classification with deep convolutional neural networks

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Deep Low-Resolution Person Re-Identification

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AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Adam: A Method for Stochastic Optimization

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What points at this work

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

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EGARNet: adjacent residual lightweight super-resolution network based on extended group-enhanced convolution

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

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DRN-VideoSR: a deep recursive network for video super-resolution based on a deformable convolution shared-assignment network

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Traditional and deep-learning-based denoising methods for medical images

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

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CVSR: complex-valued networks for video super-resolution

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Dual Attention with the Self-Attention Alignment for Efficient Video Super-resolution

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Pre‐training of gated convolution neural network for remote sensing image super‐resolution

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Learning to Have an Ear for Face Super-Resolution

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

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M-LVC: Multiple Frames Prediction for Learned Video Compression

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Learning Trajectory-Aware Transformer for Video Super-Resolution

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Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling

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STDAN: Deformable Attention Network for Space-Time Video Super-Resolution

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Asymmetric CNN for Image Superresolution

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Space-time super-resolution with motion-perceptive deformable alignment

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Video super-resolution with fused local and nonlocal feature

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Video spatio-temporal generative adversarial network for local action generation

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Structure-aware Meta-fusion for Image Super-resolution

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Guided Spatio-Temporal Learning Method for 4K Video Super-Resolution

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Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting

inferred← citeshttps://doi.org/10.1145/3649315openalex-snapshot

VertexShuffle-Based Spherical Super-Resolution for 360-Degree Videos

inferred← citeshttps://doi.org/10.1155/2021/9999398openalex-snapshot

Towards Efficient Video Detection Object Super-Resolution with Deep Fusion Network for Public Safety

inferred← citeshttps://doi.org/10.1177/0161734620910163openalex-snapshot

Temporal Super-resolution of Ultrasound Imaging Using Matrix Completion

inferred← citeshttps://doi.org/10.11834/jig.220130openalex-snapshot

Deep learning based video-related super-resolution technique： a survey

inferred← citeshttps://doi.org/10.15407/jai2022.01.288openalex-snapshot

Hybrid neural networks for noise reductions of integrated navigation complexes

inferred← citeshttps://doi.org/10.1609/aaai.v34i07.6788openalex-snapshot

FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss

inferred← citeshttps://doi.org/10.1609/aaai.v34i07.6934openalex-snapshot

Video Face Super-Resolution with Motion-Adaptive Feedback Cell

inferred← citeshttps://doi.org/10.1609/aaai.v35i3.16310openalex-snapshot

Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling

inferred← citeshttps://doi.org/10.17771/pucrio.acad.60905openalex-snapshot

RECONSTRUÇÃO DE MÚSICAS ALTAMENTE DEGRADADAS USANDO MODELOS DE APRENDIZADO PROFUNDO

inferred← citeshttps://doi.org/10.18653/v1/2020.emnlp-main.145openalex-snapshot

BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues

inferred← citeshttps://doi.org/10.21203/rs.3.rs-1845260/v1openalex-snapshot

Optimization on Video Super Resolution using Predictive Analytics with Machine Learning Concepts

inferred← citeshttps://doi.org/10.2352/ei.2022.34.8.image-287openalex-snapshot

Correspondences for image and video reconstruction

inferred← citeshttps://doi.org/10.3390/app13010526openalex-snapshot

Video Super-Resolution with Regional Focus for Recurrent Network

inferred← citeshttps://doi.org/10.3390/e23111398openalex-snapshot

S2A: Scale-Attention-Aware Networks for Video Super-Resolution

inferred← citeshttps://doi.org/10.3390/electronics11091499openalex-snapshot

Video Super-Resolution Using Multi-Scale and Non-Local Feature Fusion

inferred← citeshttps://doi.org/10.3390/electronics9122085openalex-snapshot

Bidirectional Temporal-Recurrent Propagation Networks for Video Super-Resolution

inferred← citeshttps://doi.org/10.3390/math9222873openalex-snapshot

DSTnet: Deformable Spatio-Temporal Convolutional Residual Network for Video Super-Resolution

inferred← citeshttps://doi.org/10.3390/sym17081351openalex-snapshot

Video Stabilization Algorithm Based on View Boundary Synthesis

inferred← citeshttps://doi.org/10.35940/ijrte.c1313.1183s319openalex-snapshot

Align-Filter & Learn Video Super Resolution using Deep learning (AFLVSR)

inferred← citeshttps://doi.org/10.36227/techrxiv.23896986openalex-snapshot

A Methodical Study of Deep Learning Based Video Super-Resolution

inferred← citeshttps://doi.org/10.36227/techrxiv.23896986.v1openalex-snapshot

A Methodical Study of Deep Learning Based Video Super-Resolution

inferred← citeshttps://doi.org/10.4018/ijswis.321751openalex-snapshot

A Cloud-Edge Collaborative Gaming Framework Using AI-Powered Foveated Rendering and Super Resolution

inferred← citeshttps://doi.org/10.48550/arxiv.2001.04609openalex-snapshot

Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution

inferred← citeshttps://doi.org/10.48550/arxiv.2003.02115openalex-snapshot

VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge

inferred← citeshttps://doi.org/10.48550/arxiv.2011.03802openalex-snapshot

Symmetric Parallax Attention for Stereo Image Super-Resolution

inferred← citeshttps://doi.org/10.48550/arxiv.2108.03635openalex-snapshot

Efficient Light Field Reconstruction via Spatio-Angular Dense Network

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.1/reviews/1openalex-snapshot

Peer Review #1 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.1/reviews/2openalex-snapshot

Peer Review #2 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.1/reviews/3openalex-snapshot

Peer Review #3 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.1/reviews/4openalex-snapshot

Peer Review #4 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.2/reviews/1openalex-snapshot

Peer Review #1 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.2/reviews/2openalex-snapshot

Peer Review #2 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.2/reviews/3openalex-snapshot

Peer Review #3 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.621v0.2/reviews/4openalex-snapshot

Peer Review #4 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

inferred← citeshttps://doi.org/10.7717/peerj-cs.621openalex-snapshot

A comprehensive review of deep learning-based single image super-resolution

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.