ArticlePeerJ. Computer science2021
A comprehensive review of deep learning-based single image super-resolution.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Scale-aware feature modeling and dynamic sampling enable arbitrary-scale image super-resolution.Scientific reports · 2026Article
- Light-RepViTSR: ultra-lightweight super-resolution for real-time photoacoustic endoscopy in tumor biopsy.Frontiers in bioengineering and biotechnology · 2026Article
- Hierarchical sparse Bayesian learning with adaptive Laplacian prior for single image super-resolution.Scientific reports · 2025Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- A Sub-Pixel Measurement Platform Using Twist-Angle Analysis in Two-Dimensional Planes.Sensors (Basel, Switzerland) · 2025Article
- UAV multi-source data fusion with super-resolution for accurate soybean leaf area index estimation.Frontiers in plant science · 2025Article
- Article
- Article
- A Novel Simulation Method for 3D Digital-Image Correlation: Combining Virtual Stereo Vision and Image Super-Resolution Reconstruction.Sensors (Basel, Switzerland) · 2024Article
- PlantSR: Super-Resolution Improves Object Detection in Plant Images.Journal of imaging · 2024Article
- Article
- Exponential Fusion of Interpolated Frames Network (EFIF-Net): Advancing Multi-Frame Image Super-Resolution with Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- A Multi-Scale Recursive Attention Feature Fusion Network for Image Super-Resolution Reconstruction Algorithm.Sensors (Basel, Switzerland) · 2023Article
- OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution.Journal of imaging · 2023Article
- Twinned Residual Auto-Encoder (TRAE)-A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images.Expert systems with applications · 2023Article
- How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study.The Journal of supercomputing · 2023Article
- A fine-tuned YOLOv5 deep learning approach for real-time house number detection.PeerJ. Computer science · 2023Article
- Reconstructing high fidelity digital rock images using deep convolutional neural networks.Scientific reports · 2022Article
- Impact of color augmentation and tissue type in deep learning for hematoxylin and eosin image super resolution.Journal of pathology informatics · 2022Article
Corrections and comments
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.