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Lightweight Single Image Super-resolution with Dense Connection Distillation Network

https://doi.org/10.1145/3414838
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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/10.1007/978-3-030-01249-6_16openalex-snapshot
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
inferredcites →https://doi.org/10.1007/978-3-030-11021-5_5openalex-snapshot
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
inferredcites →https://doi.org/10.1007/978-3-319-46475-6_25openalex-snapshot
Accelerating the Super-Resolution Convolutional Neural Network
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/10.1109/34.730558openalex-snapshot
A model of saliency-based visual attention for rapid scene analysis
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.2016.181openalex-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.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.2017.298openalex-snapshot
Image Super-Resolution via Deep Recursive Residual Network
inferredcites →https://doi.org/10.1109/cvpr.2017.618openalex-snapshot
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr.2018.00082openalex-snapshot
Fast and Accurate Single Image Super-Resolution via Information Distillation 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.2018.00745openalex-snapshot
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.01132openalex-snapshot
Second-Order Attention Network 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.2001.937655openalex-snapshot
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
inferredcites →https://doi.org/10.1109/iccv.2013.241openalex-snapshot
Anchored Neighborhood Regression for Fast Example-Based 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/iccv.2017.486openalex-snapshot
MemNet: A Persistent Memory Network for Image Restoration
inferredcites →https://doi.org/10.1109/iccv.2017.514openalex-snapshot
Image Super-Resolution Using Dense Skip Connections
inferredcites →https://doi.org/10.1109/icip.1997openalex-snapshot
Proceedings of International Conference on Image Processing
inferredcites →https://doi.org/10.1109/ictsd.2015.7095900openalex-snapshot
Super resolution techniques for medical image processing
inferredcites →https://doi.org/10.1109/tcyb.2018.2813971openalex-snapshot
Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition
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.2006.877407openalex-snapshot
An edge-guided image interpolation algorithm via directional filtering and data fusion
inferredcites →https://doi.org/10.1109/tip.2011.2162423openalex-snapshot
Very Low Resolution Face Recognition Problem
inferredcites →https://doi.org/10.1109/tip.2012.2208977openalex-snapshot
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression
inferredcites →https://doi.org/10.1109/tip.2017.2655449openalex-snapshot
Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval
inferredcites →https://doi.org/10.1109/tip.2018.2878970openalex-snapshot
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
inferredcites →https://doi.org/10.1109/tip.2019.2940684openalex-snapshot
Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification
inferredcites →https://doi.org/10.1109/tmm.2012.2185041openalex-snapshot
Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification
inferredcites →https://doi.org/10.1109/tmm.2012.2187181openalex-snapshot
Movie2Comics: Towards a Lively Video Content Presentation
inferredcites →https://doi.org/10.1109/tmm.2018.2877886openalex-snapshot
Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification
inferredcites →https://doi.org/10.1109/tnnls.2020.2979190openalex-snapshot
Deep Coattention-Based Comparator for Relative Representation Learning in Person Re-Identification
inferredcites →https://doi.org/10.1109/tpami.2015.2439281openalex-snapshot
Image Super-Resolution Using Deep Convolutional Networks
inferredcites →https://doi.org/10.1145/3343031.3351084openalex-snapshot
Lightweight Image Super-Resolution with Information Multi-distillation Network
inferredcites →https://doi.org/10.1175/1520-0450(1979)018<1016:lfioat>2.0.co;2openalex-snapshot
Lanczos Filtering in One and Two Dimensions
inferredcites →https://doi.org/10.48550/arxiv.1603.09056openalex-snapshot
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
inferredcites →https://doi.org/10.5244/c.26.135openalex-snapshot
Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-031-25063-7_36openalex-snapshot
Real-Time Channel Mixing Net for Mobile Image Super-Resolution
inferred← citeshttps://doi.org/10.1007/s00034-024-02975-wopenalex-snapshot
GRFN: A Group Residual Feature Network for Lightweight Image Super-Resolution
inferred← citeshttps://doi.org/10.1038/s41598-023-41484-9openalex-snapshot
Deep neural architecture for natural language image synthesis for Tamil text using BASEGAN and hybrid super resolution GAN (HSRGAN)
inferred← citeshttps://doi.org/10.1109/access.2022.3183634openalex-snapshot
Edge Computing Technology Enablers: A Systematic Lecture Study
inferred← citeshttps://doi.org/10.1109/tcbb.2022.3229114openalex-snapshot
VMF-SSD: A Novel V-Space Based Multi-Scale Feature Fusion SSD for Apple Leaf Disease Detection
inferred← citeshttps://doi.org/10.1109/tgrs.2024.3422474openalex-snapshot
A Lightweight Network With Latent Representations for UAV Thermal Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/tmm.2021.3134172openalex-snapshot
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution
inferred← citeshttps://doi.org/10.1145/3569900openalex-snapshot
Lightweight Feature De-redundancy and Self-calibration Network for Efficient Image Super-resolution
inferred← citeshttps://doi.org/10.1145/3570164openalex-snapshot
Efficient Single-image Super-resolution Using Dual path Connections with Multiple scale Learning
inferred← citeshttps://doi.org/10.1145/3592613openalex-snapshot
Recurrent Multi-scale Approximation-Guided Network for Single Image Super-Resolution
inferred← citeshttps://doi.org/10.1145/3595924openalex-snapshot
Text Image Super-Resolution Guided by Text Structure and Embedding Priors
inferred← citeshttps://doi.org/10.1145/3616376openalex-snapshot
An Image Arbitrary-Scale Super-Resolution Network Using Frequency-domain Information
inferred← citeshttps://doi.org/10.1145/3651309openalex-snapshot
Enhanced Video Super-Resolution Network towards Compressed Data
inferred← citeshttps://doi.org/10.1145/3690387openalex-snapshot
Cross-Modal Face Super-Resolution Based on Quasi-Siamese Domain Transfer Fusion Network
inferred← citeshttps://doi.org/10.1145/3769431openalex-snapshot
Empowering Personalized and Privacy-Preserving for Image Super-Resolution with Decentralized Collaboration
inferred← citeshttps://doi.org/10.3837/tiis.2022.09.003openalex-snapshot
Cascaded Residual Densely Connected Network for Image Super-Resolution
inferred← citeshttps://doi.org/10.3934/era.2025055openalex-snapshot
Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention
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