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Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution

https://doi.org/10.1145/3532864
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot
Visualizing and Understanding Convolutional 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/10.1007/s11263-016-0932-3openalex-snapshot
Sketch-a-Net: A Deep Neural Network that Beats Humans
inferredcites →https://doi.org/10.1016/j.image.2014.06.006openalex-snapshot
No-reference image quality assessment based on spatial and spectral entropies
inferredcites →https://doi.org/10.1016/j.patcog.2020.107475openalex-snapshot
Hierarchical dense recursive network for image super-resolution
inferredcites →https://doi.org/10.1016/j.sigpro.2020.107831openalex-snapshot
Single image super-resolution with multi-scale information cross-fusion network
inferredcites →https://doi.org/10.1109/cvpr.1994.323798openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
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.2019.00399openalex-snapshot
Feedback Network for Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvprw.2017.150openalex-snapshot
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
inferredcites →https://doi.org/10.1109/iccv.1995.466933openalex-snapshot
Proceedings of IEEE International Conference on Computer Vision
inferredcites →https://doi.org/10.1109/iccv.2019.00428openalex-snapshot
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution
inferredcites →https://doi.org/10.1109/jstsp.2020.3045282openalex-snapshot
Multi-Scale Image Super-Resolution Via a Single Extendable Deep Network
inferredcites →https://doi.org/10.1109/lsp.2012.2227726openalex-snapshot
Making a “Completely Blind” Image Quality Analyzer
inferredcites →https://doi.org/10.1109/tbc.2020.3028356openalex-snapshot
Lightweight Image Super-Resolution by Multi-Scale Aggregation
inferredcites →https://doi.org/10.1109/tcsvt.2019.2915238openalex-snapshot
Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/tcsvt.2020.2988895openalex-snapshot
Multi-Grained Attention Networks for Single Image Super-Resolution
inferredcites →https://doi.org/10.1109/tcsvt.2020.3027732openalex-snapshot
MDCN: Multi-Scale Dense Cross Network for 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/tgrs.2020.3019660openalex-snapshot
Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale Networks
inferredcites →https://doi.org/10.1109/tip.2020.3014953openalex-snapshot
s-LWSR: Super Lightweight Super-Resolution Network
inferredcites →https://doi.org/10.1109/tip.2020.3043093openalex-snapshot
Learning Spatial Attention for Face Super-Resolution
inferredcites →https://doi.org/10.1109/tmi.2016.2528162openalex-snapshot
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
inferredcites →https://doi.org/10.1109/tnnls.2019.2920852openalex-snapshot
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single-Image Super-Resolution
inferredcites →https://doi.org/10.1109/tpami.2015.2439281openalex-snapshot
Image Super-Resolution Using Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/tpami.2018.2865304openalex-snapshot
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
inferredcites →https://doi.org/10.1109/tpami.2019.2913372openalex-snapshot
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.1109/tpami.2020.2968521openalex-snapshot
Residual Dense Network for Image Restoration
inferredcites →https://doi.org/10.1109/tpami.2020.2982166openalex-snapshot
Deep Learning for Image Super-Resolution: A Survey
inferredcites →https://doi.org/10.1109/tpami.2020.3021088openalex-snapshot
Densely Residual Laplacian Super-Resolution
inferredcites →https://doi.org/10.1145/3177756openalex-snapshot
Full 3D Reconstruction of Non-Rigidly Deforming Objects
inferredcites →https://doi.org/10.1145/3377352openalex-snapshot
Enforcing Affinity Feature Learning through Self-attention for Person Re-identification
inferredcites →https://doi.org/10.1145/3377874openalex-snapshot
A Deep Learning Approach for Face Hallucination Guided by Facial Boundary Responses
inferredcites →https://doi.org/10.1145/3398685openalex-snapshot
Kernel Attention Network for Single Image Super-Resolution
inferredcites →https://doi.org/10.48550/arxiv.1904.05677openalex-snapshot
Deep Back-Projection Networks for Single Image Super-resolution
inferredcites →https://doi.org/10.48550/arxiv.2010.02631openalex-snapshot
Unfolding the Alternating Optimization for Blind Super Resolution
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/10.1016/j.displa.2024.102782openalex-snapshot
Light field image super-resolution using a Content-Aware Spatial–Angular Interaction network
inferred← citeshttps://doi.org/10.1016/j.rineng.2025.106871openalex-snapshot
HySim: An efficient hybrid similarity measure for patch matching in image inpainting
inferred← citeshttps://doi.org/10.1109/inocon60754.2024.10511465openalex-snapshot
Hybrid Perceptual Structural and Pixelwise Residual Loss Function based Image Super-Resolution
inferred← citeshttps://doi.org/10.1109/tai.2022.3225784openalex-snapshot
Image Super-Resolution With Content-Aware Feature Processing
inferred← citeshttps://doi.org/10.1145/3589963openalex-snapshot
Compressed Screen Content Image Super Resolution
inferred← citeshttps://doi.org/10.1145/3592613openalex-snapshot
Recurrent Multi-scale Approximation-Guided Network for Single Image Super-Resolution
inferred← citeshttps://doi.org/10.1145/3616376openalex-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/10.1145/3703632openalex-snapshot
DIRformer: A Novel Image Restoration Approach Based on U-shaped Transformer and Diffusion Models
inferred← citeshttps://doi.org/10.18280/ria.360616openalex-snapshot
Study of Deep Learning-based models for Single Image Super-Resolution
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