On the public citation graph
Deformable Non-Local Network for Video Super-Resolution
0 signed · 119 inferred · view as graph
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.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
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/10.1007/978-3-030-11021-5_5openalex-snapshot
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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/10.1007/978-3-319-46475-6_25openalex-snapshot
Accelerating the Super-Resolution Convolutional Neural Network
inferredcites →https://doi.org/10.1007/s11263-018-01144-2openalex-snapshot
Video Enhancement with Task-Oriented Flow
inferredcites →https://doi.org/10.1016/j.ins.2020.08.114openalex-snapshot
Progressive perception-oriented network for single image super-resolution
inferredcites →https://doi.org/10.1109/access.2017.2737418openalex-snapshot
Eyes on the Target: Super-Resolution and License-Plate Recognition in Low-Quality Surveillance Videos
inferredcites →https://doi.org/10.1109/access.2018.2797197openalex-snapshot
High Dynamic Range and Super-Resolution Imaging From a Single Image
inferredcites →https://doi.org/10.1109/access.2018.2829908openalex-snapshot
Video Super-Resolution via Residual Learning
inferredcites →https://doi.org/10.1109/access.2019.2930293openalex-snapshot
SKFlow: Optical Flow Estimation Using Selective Kernel Networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7299159openalex-snapshot
Handling motion blur in multi-frame super-resolution
inferredcites →https://doi.org/10.1109/cvpr.2016.181openalex-snapshot
Deeply-Recursive Convolutional Network for Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr.2016.182openalex-snapshot
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
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.2017.179openalex-snapshot
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.291openalex-snapshot
Optical Flow Estimation Using a Spatial Pyramid Network
inferredcites →https://doi.org/10.1109/cvpr.2017.298openalex-snapshot
Image Super-Resolution via Deep Recursive Residual Network
inferredcites →https://doi.org/10.1109/cvpr.2017.304openalex-snapshot
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
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.2018.00936openalex-snapshot
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
inferredcites →https://doi.org/10.1109/cvpr.2019.00402openalex-snapshot
Recurrent Back-Projection Network for Video Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr.2019.00953openalex-snapshot
Deformable ConvNets V2: More Deformable, Better Results
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/cvprw.2017.149openalex-snapshot
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
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.2018.00130openalex-snapshot
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
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.2015.316openalex-snapshot
FlowNet: Learning Optical Flow with Convolutional Networks
inferredcites →https://doi.org/10.1109/iccv.2015.68openalex-snapshot
Video Super-Resolution via Deep Draft-Ensemble Learning
inferredcites →https://doi.org/10.1109/iccv.2017.274openalex-snapshot
Robust Video Super-Resolution with Learned Temporal Dynamics
inferredcites →https://doi.org/10.1109/iccv.2017.479openalex-snapshot
Detail-Revealing Deep Video Super-Resolution
inferredcites →https://doi.org/10.1109/iccv.2017.89openalex-snapshot
Deformable Convolutional Networks
inferredcites →https://doi.org/10.1109/msp.2003.1203207openalex-snapshot
Super-resolution image reconstruction: a technical overview
inferredcites →https://doi.org/10.1109/tci.2016.2532323openalex-snapshot
Video Super-Resolution With Convolutional Neural Networks
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.2004.834669openalex-snapshot
Fast and Robust Multiframe Super Resolution
inferredcites →https://doi.org/10.1109/tpami.2013.127openalex-snapshot
On Bayesian Adaptive Video Super Resolution
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/ima.22941openalex-snapshot
RETRACTED: Toward more accurate diagnosis of multiple sclerosis: Automated lesion segmentation in brain magnetic resonance image using modified U‐Net model
inferred← citeshttps://doi.org/10.1007/978-3-030-66823-5_5openalex-snapshot
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution
inferred← citeshttps://doi.org/10.1007/978-3-030-66823-5_7openalex-snapshot
Deep Plug-and-Play Video Super-Resolution
inferred← citeshttps://doi.org/10.1007/978-3-030-81119-8_4openalex-snapshot
Investigation of Super-Resolution Methods for Processing a Sequence of Aerial Photographs
inferred← citeshttps://doi.org/10.1007/978-3-030-86383-8_51openalex-snapshot
Efficient Spatio-Temporal Network with Gated Fusion for Video Super-Resolution
inferred← citeshttps://doi.org/10.1007/978-3-031-40070-4_13openalex-snapshot
NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection
inferred← citeshttps://doi.org/10.1007/978-3-031-46317-4_15openalex-snapshot
Multi-scale Non-local Bidirectional Fusion for Video Super-Resolution
inferred← citeshttps://doi.org/10.1007/978-3-031-72855-6_21openalex-snapshot
Data Overfitting for On-device Super-Resolution with Dynamic Algorithm and Compiler Co-design
inferred← citeshttps://doi.org/10.1007/978-981-97-5492-2_9openalex-snapshot
DSCVSR: A Lightweight Video Super-Resolution for Arbitrary Magnification
inferred← citeshttps://doi.org/10.1007/978-981-99-0856-1_25openalex-snapshot
Deep Video Matting with Temporal Consistency
inferred← citeshttps://doi.org/10.1007/s00530-024-01528-0openalex-snapshot
A lightweight distillation recurrent convolution network on FPGA for real-time video super-resolution
inferred← citeshttps://doi.org/10.1007/s10462-021-10039-7openalex-snapshot
Artificial intelligence in the creative industries: a review
inferred← citeshttps://doi.org/10.1007/s10462-022-10147-yopenalex-snapshot
Video super-resolution based on deep learning: a comprehensive survey
inferred← citeshttps://doi.org/10.1007/s10489-023-05131-0openalex-snapshot
An optical flow estimation method based on multiscale anisotropic convolution
inferred← citeshttps://doi.org/10.1007/s10791-026-09903-3openalex-snapshot
Early detection of anorexia from reddit posts using time series based transformer model
inferred← citeshttps://doi.org/10.1007/s13042-025-02543-xopenalex-snapshot
Improving bi-directional recurrent network for video super-resolution with deformable motion alignment structure
inferred← citeshttps://doi.org/10.1007/s42979-024-02710-xopenalex-snapshot
Frame Selection Using Spatiotemporal Dynamics and Key Features as Input Pre-processing for Video Super-Resolution Models
inferred← citeshttps://doi.org/10.1016/b978-0-32-398370-9.00008-1openalex-snapshot
Video enhancement and super-resolution
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110571openalex-snapshot
DAINet: Dual-alignment implicit network for accelerated multi-contrast MRI reconstruction
inferred← citeshttps://doi.org/10.1016/j.dsp.2025.105098openalex-snapshot
HAMSA: Hybrid attention transformer and multi-scale alignment aggregation network for video super-resolution
inferred← citeshttps://doi.org/10.1016/j.ijnaoe.2022.100472openalex-snapshot
Fender segmentation in unmanned aerial vehicle images based on densely connected receptive field block
inferred← citeshttps://doi.org/10.1016/j.image.2024.117175openalex-snapshot
A ‘deep’ review of video super-resolution
inferred← citeshttps://doi.org/10.1016/j.imavis.2025.105721openalex-snapshot
DNLN: Image super-resolution with Deformable Non-Local attention and Multi-Branch Weighted Feature Fusion
inferred← citeshttps://doi.org/10.1016/j.neucom.2022.02.067openalex-snapshot
High-resolution optical flow and frame-recurrent network for video super-resolution and deblurring
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126355openalex-snapshot
Online Video Super-resolution using Information Replenishing Unidirectional Recurrent Model
inferred← citeshttps://doi.org/10.1038/s41524-024-01313-7openalex-snapshot
Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning
inferred← citeshttps://doi.org/10.1049/cit2.12191openalex-snapshot
Deeply‐Recursive Attention Network for video steganography
inferred← citeshttps://doi.org/10.1049/ipr2.12134openalex-snapshot
Video super‐resolution with non‐local alignment network
inferred← citeshttps://doi.org/10.1109/access.2021.3112488openalex-snapshot
Light Field Image Super-Resolution via Mutual Attention Guidance
inferred← citeshttps://doi.org/10.1109/access.2023.3316510openalex-snapshot
FGBRSN: Flow-Guided Gated Bi-Directional Recurrent Separated Network for Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/access.2024.3406476openalex-snapshot
A Novel Zero-Shot Real World Spatio-Temporal Super-Resolution (ZS-RW-STSR) Model for Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/comst.2024.3427360openalex-snapshot
A Survey on Intelligent Solutions for Increased Video Delivery Quality in Cloud–Edge–End Networks
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00361openalex-snapshot
Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00491openalex-snapshot
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00588openalex-snapshot
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01689openalex-snapshot
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00171openalex-snapshot
Learning Distortion Invariant Representation for Image Restoration from a Causality Perspective
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00989openalex-snapshot
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
inferred← citeshttps://doi.org/10.1109/cvprw53098.2021.00075openalex-snapshot
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
inferred← citeshttps://doi.org/10.1109/eexpolytech56308.2022.9950964openalex-snapshot
Application of Deep Learning Methods to Improve the Resolution of Small Objects Images in Aerial Photographs
inferred← citeshttps://doi.org/10.1109/iaecst60924.2023.10503364openalex-snapshot
FATN: Flow-Guided Alignment-Based Transformer Network for High Dynamic Range Imaging
inferred← citeshttps://doi.org/10.1109/iccc62609.2024.10942095openalex-snapshot
Video Super-Resolution Based on Multi-Flow Guided Deformable Alignment and Temporal-Spatial Fusion
inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897627openalex-snapshot
Multi-Stage Feature Alignment Network for Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/jproc.2025.3613233openalex-snapshot
Deep-Learning-Empowered Super Resolution: A Comprehensive Survey and Future Prospects
inferred← citeshttps://doi.org/10.1109/mmsp55362.2022.9949300openalex-snapshot
Deep Video Super-Resolution with Flow-Guided Deformable Alignment and Sparsity-based Temporal-Spatial Enhancement
inferred← citeshttps://doi.org/10.1109/slaai-icai68534.2025.11318427openalex-snapshot
A Flash-Attention-Optimized CNN Transformer Framework for Real-Time Video Restoration
inferred← citeshttps://doi.org/10.1109/tcsi.2023.3258446openalex-snapshot
An Efficient Accelerator Based on Lightweight Deformable 3D-CNN for Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/tcsvt.2024.3379971openalex-snapshot
Exploiting Bidirectional Quality Impulse for Reference Picture Resampled Gaming Video Coding
inferred← citeshttps://doi.org/10.1109/tgrs.2024.3354255openalex-snapshot
RDNRnet: A Reconstruction Solution of NDVI Based on SAR and Optical Images by Residual-in-Residual Dense Blocks
inferred← citeshttps://doi.org/10.1109/tim.2023.3260278openalex-snapshot
A Multiscale Aligned Video Super-Resolution Network for Improving Vibration Signal Measurement Accuracy
inferred← citeshttps://doi.org/10.1109/tip.2020.3042059openalex-snapshot
Light Field Image Super-Resolution Using Deformable Convolution
inferred← citeshttps://doi.org/10.1109/tip.2026.3702419openalex-snapshot
Large Language Model Guided Progressive Feature Alignment for Multimodal UAV Object Detection
inferred← citeshttps://doi.org/10.1109/tmm.2026.3684752openalex-snapshot
TSNet: An Image Enhancement Model for Vehicle Patrol
inferred← citeshttps://doi.org/10.1109/tnnls.2023.3243029openalex-snapshot
STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/tnnls.2023.3347450openalex-snapshot
DVSRNet: Deep Video Super-Resolution Based on Progressive Deformable Alignment and Temporal-Sparse Enhancement
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3378420openalex-snapshot
Real-World Light Field Image Super-Resolution Via Degradation Modulation
inferred← citeshttps://doi.org/10.1109/tpami.2023.3293522openalex-snapshot
CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/tvcg.2022.3214420openalex-snapshot
Deep Hierarchical Super Resolution for Scientific Data
inferred← citeshttps://doi.org/10.1109/wcsp52459.2021.9613243openalex-snapshot
Gated Bi-Recurrent Separate Network for Video Super-Resolution
inferred← citeshttps://doi.org/10.1117/1.jei.30.3.033020openalex-snapshot
Space-time super-resolution with motion-perceptive deformable alignment
inferred← citeshttps://doi.org/10.1145/3474085.3475672openalex-snapshot
How Video Super-Resolution and Frame Interpolation Mutually Benefit
inferred← citeshttps://doi.org/10.11728/cjss2026.01.2025-0002openalex-snapshot
Video Super-resolution Method for Spacecraft Approaching and Detecting Asteroids
inferred← citeshttps://doi.org/10.11834/jig.220130openalex-snapshot
Deep learning based video-related super-resolution technique： a survey
inferred← citeshttps://doi.org/10.1609/aaai.v35i2.16181openalex-snapshot
Understanding Deformable Alignment in Video Super-Resolution
inferred← citeshttps://doi.org/10.2352/ei.2022.34.14.coimg-247openalex-snapshot
OldVSR: A model for the video super-resolution and restoration of old real-world TV series
inferred← citeshttps://doi.org/10.26599/cvm.2025.9450502openalex-snapshot
Spatiotemporal Fusion Transformer for Video Demoiréing
inferred← citeshttps://doi.org/10.3390/drones8040144openalex-snapshot
An Efficient Adjacent Frame Fusion Mechanism for Airborne Visual Object Detection
inferred← citeshttps://doi.org/10.3390/info11080391openalex-snapshot
Deep Learning for Facial Beauty Prediction
inferred← citeshttps://doi.org/10.3390/jsan12020026openalex-snapshot
Fusion Objective Function on Progressive Super-Resolution Network
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/s20236733openalex-snapshot
Multi-Scale Shape Adaptive Network for Raindrop Detection and Removal from a Single Image
inferred← citeshttps://doi.org/10.36227/techrxiv.22591426openalex-snapshot
Spatiotemporal Dynamics and Frame Features for Improved Input Selection in Video Super-Resolution Models
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/10.5194/isprs-archives-xliii-b3-2021-497-2021openalex-snapshot
COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE
inferred← citeshttps://doi.org/10.58837/chula.the.2021.128openalex-snapshot
Single image super-resolution using capsule generative adversarial network
inferred← citeshttps://doi.org/10.58837/chula.the.2022.134openalex-snapshot
Deep consecutive attention network for video 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.