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Seven Ways to Improve Example-Based Single Image Super Resolution
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Outgoing claims: the papers this work cites, builds on, or argues with.
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/10.1007/978-3-319-10602-1_4openalex-snapshot
Deep Network Cascade for Image Super-resolution
inferredcites →https://doi.org/10.1007/978-3-319-16811-1_23openalex-snapshot
Fast Super-Resolution via Dense Local Training and Inverse Regressor Search
inferredcites →https://doi.org/10.1007/978-3-319-16817-3_8openalex-snapshot
A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
inferredcites →https://doi.org/10.1007/978-3-642-19318-7_39openalex-snapshot
Exploiting Self-similarities for Single Frame 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/10.1016/1049-9652(91)90045-lopenalex-snapshot
Improving resolution by image registration
inferredcites →https://doi.org/10.1016/b978-012077790-7/50030-8openalex-snapshot
Image Interpolation and Resampling
inferredcites →https://doi.org/10.1016/j.cviu.2015.09.008openalex-snapshot
Semantic super-resolution: When and where is it useful?
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cvpr.2001.990517openalex-snapshot
Rapid object detection using a boosted cascade of simple features
inferredcites →https://doi.org/10.1109/cvpr.2004.1315043openalex-snapshot
Super-resolution through neighbor embedding
inferredcites →https://doi.org/10.1109/cvpr.2008.4587647openalex-snapshot
Image super-resolution as sparse representation of raw image patches
inferredcites →https://doi.org/10.1109/cvpr.2010.5540206openalex-snapshot
Context-constrained hallucination for image super-resolution
inferredcites →https://doi.org/10.1109/cvpr.2013.141openalex-snapshot
Fast Image Super-Resolution Based on In-Place Example Regression
inferredcites →https://doi.org/10.1109/cvpr.2014.373openalex-snapshot
Single Image Super-resolution Using Deformable Patches
inferredcites →https://doi.org/10.1109/cvpr.2015.7299003openalex-snapshot
Fast and accurate image upscaling with super-resolution forests
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.2016.206openalex-snapshot
Seven Ways to Improve Example-Based Single Image Super Resolution
inferredcites →https://doi.org/10.1109/iccphot.2013.6528298openalex-snapshot
Combining the power of Internal and External denoising
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.2009.5459271openalex-snapshot
Super-resolution from a single image
inferredcites →https://doi.org/10.1109/iccv.2013.121openalex-snapshot
Nonparametric Blind Super-resolution
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.2013.352openalex-snapshot
Accurate Blur Models vs. Image Priors in Single Image Super-resolution
inferredcites →https://doi.org/10.1109/iccv.2015.50openalex-snapshot
Deep Networks for Image Super-Resolution with Sparse Prior
inferredcites →https://doi.org/10.1109/iccv.2015.67openalex-snapshot
Conditioned Regression Models for Non-blind Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/icip.2015.7351454openalex-snapshot
Efficient regression priors for post-processing demosaiced images
inferredcites →https://doi.org/10.1109/tip.2008.2008067openalex-snapshot
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
inferredcites →https://doi.org/10.1109/tip.2010.2050625openalex-snapshot
Image Super-Resolution Via Sparse Representation
inferredcites →https://doi.org/10.1109/tip.2012.2235847openalex-snapshot
Nonlocally Centralized Sparse Representation for Image Restoration
inferredcites →https://doi.org/10.1109/tip.2014.2305844openalex-snapshot
A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/tip.2015.2389629openalex-snapshot
Learning Multiple Linear Mappings for Efficient Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/tpami.2010.25openalex-snapshot
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
inferredcites →https://doi.org/10.1109/tpami.2015.2439281openalex-snapshot
Image Super-Resolution Using Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/tsp.2006.881199openalex-snapshot
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
inferredcites →https://doi.org/10.1111/cgf.12544openalex-snapshot
Jointly Optimized Regressors for Image Super‐resolution
inferredcites →https://doi.org/10.1145/1944846.1944852openalex-snapshot
Image and video upscaling from local self-examples
inferredcites →https://doi.org/10.5244/c.26.135openalex-snapshot
Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding
inferredcites →https://doi.org/10.5244/c.28.6openalex-snapshot
Return of the Devil in the Details: Delving Deep into Convolutional Nets
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Projection‐transformation method with considering energy conservation
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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Residual Feature Distillation Network for Lightweight Image Super-Resolution
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Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution
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Deep Atrous Guided Filter for Image Restoration in Under Display Cameras
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Self-supervised Graph Representation Learning with Variational Inference
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A Multi-resolution Medical Image Fusion Network with Iterative Back-Projection
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Task-Driven Super Resolution: Object Detection in Low-Resolution Images
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Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
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AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
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Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challenge: Report
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AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results
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Learning a Mixture of Deep Networks for Single Image Super-Resolution
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Generic 3D Convolutional Fusion for Image Restoration
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Robust Noisy Image Super-Resolution Using $$\ell _1$$ -norm Regularization and Non-local Constraint
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AWCR: Adaptive and Weighted Collaborative Representations for Face Super-Resolution with Context Residual-Learning
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Single Image Super Resolution via Neighbor Reconstruction
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Single-Image Super-Resolution: A Survey
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Single Image Super Resolution via Deep Convolutional Dual Upscaling Branches with Different Focus
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Improving the Quality of X-ray Images of Seeds in Smart Farming Using Deep Learning
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Lightweight global-locally connected distillation network for single image super-resolution
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High Resolution Similarity Directed Adjusted Anchored Neighborhood Regression for Single Image Super-Resolution
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Single Image Super Resolution Model Using Learnable Weight Factor in Residual Skip Connection
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Multi-Scale Residual Hierarchical Dense Networks for Single Image Super-Resolution
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Color Filter Array Demosaicking Using Densely Connected Residual Network
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Lightweight Single Image Super-Resolution With Multi-Scale Spatial Attention Networks
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Adaptive Diagonal Total-Variation Generative Adversarial Network for Super-Resolution Imaging
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A Residual Dense U-Net Neural Network for Image Denoising
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Single-Shot High Dynamic Range Imaging via Multiscale Convolutional Neural Network
inferred← citeshttps://doi.org/10.1109/access.2021.3105726openalex-snapshot
From Local to Global: Efficient Dual Attention Mechanism for Single Image Super-Resolution
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Three-Dimensional Video Super-Resolution Reconstruction Scheme Based on Histogram Matching and Recursive Bayesian Algorithms
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Selfie Periocular Verification Using an Efficient Super-Resolution Approach
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Arbitrary Scale Super-Resolution Neural Network Based on Residual Channel-Spatial Attention
inferred← citeshttps://doi.org/10.1109/access.2023.3328229openalex-snapshot
SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution
inferred← citeshttps://doi.org/10.1109/access.2025.3539920openalex-snapshot
CutMAA: Motion-Aware Data Augmentation for Light Field Super-Resolution
inferred← citeshttps://doi.org/10.1109/access.2026.3667149openalex-snapshot
A Multi-Scale Hybrid Attention Residual Network for Super-Resolution Image Reconstruction
inferred← citeshttps://doi.org/10.1109/apccas.2016.7803921openalex-snapshot
Effective model construction for enhanced prediction in example-based super-resolution
inferred← citeshttps://doi.org/10.1109/avss.2019.8909900openalex-snapshot
Image and Video Super Resolution using Recurrent Generative Adversarial Network
inferred← citeshttps://doi.org/10.1109/avss56176.2022.9959415openalex-snapshot
Real Image Super-Resolution using GAN through modeling of LR and HR process
inferred← citeshttps://doi.org/10.1109/cac51589.2020.9327709openalex-snapshot
Single Image Super-Resolution Using Feedback Attention Networks
inferred← citeshttps://doi.org/10.1109/ccdc58219.2023.10326617openalex-snapshot
Multi-scale feature feedback network for single image super-resolution
inferred← citeshttps://doi.org/10.1109/cisp-bmei48845.2019.8965862openalex-snapshot
3D Segmentation of Mice Gland Based on Ensemble Learning
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/cvpr.2017.298openalex-snapshot
Image Super-Resolution via Deep Recursive Residual Network
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00179openalex-snapshot
Deep Back-Projection Networks for Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00185openalex-snapshot
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00262openalex-snapshot
Residual Dense Network for Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00329openalex-snapshot
Zero-Shot Super-Resolution Using Deep Internal Learning
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00693openalex-snapshot
Frame-Recurrent Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvpr.2019.00399openalex-snapshot
Feedback Network for Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvpr.2019.01077openalex-snapshot
Fast Spatio-Temporal Residual Network for Video Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00037openalex-snapshot
Unpaired Image Super-Resolution Using Pseudo-Supervision
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00354openalex-snapshot
Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization
inferred← citeshttps://doi.org/10.1109/cvpr42600.2020.00840openalex-snapshot
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00631openalex-snapshot
Single Pair Cross-Modality Super Resolution
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00849openalex-snapshot
Adaptive Consistency Prior based Deep Network for Image Denoising
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00907openalex-snapshot
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00167openalex-snapshot
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending
inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.02386openalex-snapshot
Equivariant Plug-and-Play Image Reconstruction
inferred← citeshttps://doi.org/10.1109/cvprw.2017.141openalex-snapshot
Exploiting Reflectional and Rotational Invariance in Single Image Superresolution
inferred← citeshttps://doi.org/10.1109/cvprw.2017.142openalex-snapshot
Image Super Resolution Based on Fusing Multiple Convolution Neural Networks
inferred← citeshttps://doi.org/10.1109/cvprw.2017.147openalex-snapshot
Fast and Accurate Image Super-Resolution Using a Combined Loss
inferred← citeshttps://doi.org/10.1109/cvprw.2017.149openalex-snapshot
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2017.150openalex-snapshot
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
inferred← citeshttps://doi.org/10.1109/cvprw.2017.151openalex-snapshot
Enhanced Deep Residual Networks for Single Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2017.153openalex-snapshot
A Deep Convolutional Neural Network with Selection Units for Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2017.154openalex-snapshot
Balanced Two-Stage Residual Networks for Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2018.00130openalex-snapshot
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2018.00134openalex-snapshot
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2018.00138openalex-snapshot
NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00236openalex-snapshot
An Epipolar Volume Autoencoder With Adversarial Loss for Deep Light Field Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00246openalex-snapshot
Orientation-Aware Deep Neural Network for Real Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00247openalex-snapshot
EDVR: Video Restoration With Enhanced Deformable Convolutional Networks
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00249openalex-snapshot
NTIRE 2019 Challenge on Video Deblurring: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00250openalex-snapshot
NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00251openalex-snapshot
NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00252openalex-snapshot
Multi-Scale Deep Neural Networks for Real Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00254openalex-snapshot
SCAN: Spatial Color Attention Networks for Real Single Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00262openalex-snapshot
Deep Iterative Down-Up CNN for Image Denoising
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00263openalex-snapshot
Densely Connected Hierarchical Network for Image Denoising
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00264openalex-snapshot
Fractal Residual Network and Solutions for Real Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00273openalex-snapshot
NTIRE 2019 Challenge on Real Image Denoising: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00274openalex-snapshot
NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00275openalex-snapshot
NTIRE 2019 Challenge on Image Enhancement: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw.2019.00277openalex-snapshot
NTIRE 2019 Image Dehazing Challenge Report
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00105openalex-snapshot
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00216openalex-snapshot
NTIRE 2020 Challenge on Image and Video Deblurring
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00227openalex-snapshot
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00231openalex-snapshot
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00238openalex-snapshot
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00253openalex-snapshot
NTIRE 2020 Challenge on NonHomogeneous Dehazing
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00254openalex-snapshot
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00255openalex-snapshot
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00256openalex-snapshot
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw50498.2020.00268openalex-snapshot
Identity Enhanced Residual Image Denoising
inferred← citeshttps://doi.org/10.1109/cvprw53098.2021.00025openalex-snapshot
NTIRE 2021 Challenge on Image Deblurring
inferred← citeshttps://doi.org/10.1109/cvprw53098.2021.00070openalex-snapshot
NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw53098.2021.00072openalex-snapshot
NTIRE 2021 Learning the Super-Resolution Space Challenge
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/cvprw53098.2021.00076openalex-snapshot
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study
inferred← citeshttps://doi.org/10.1109/cvprw53098.2021.00078openalex-snapshot
NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results
inferred← citeshttps://doi.org/10.1109/cvprw53098.2021.00286openalex-snapshot
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
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