ArticleSensors (Basel, Switzerland)2023
Vision Transformers in Image Restoration: A Survey.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Vision transformer framework for host based cryptojacking malware detection.Scientific reports · 2026Article
- HybridViT for robust wheat leaf disease detection using CLAHE and attention-based feature fusion.Scientific reports · 2026Article
- GreenViT: A Vision Transformer with Single-Path Progressive Upsampling for Urban Green-Space Segmentation and Auditable Area Estimation.Journal of imaging · 2026Article
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- UNet-ECA-Bio: a biologically informed deep learning model for high-throughput micro-phenotyping of rice stem vascular bundles.Frontiers in plant science · 2026Article
- Data-efficient image transformer for landscape character classification and visual comfort prediction in Chinese hospitals.Frontiers in artificial intelligence · 2026Article
- Magnetic resonance image processing transformer for general accelerated image restoration.Scientific reports · 2025Article
- Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images.Scientific reports · 2025Article
- Binary and Multi-Class Classification of Colorectal Polyps Using CRP-ViT: A Comparative Study Between CNNs and QNNs.Life (Basel, Switzerland) · 2025Article
- Enhancing Robustness in UDC Image Restoration Through Adversarial Purification and Fine-Tuning.Sensors (Basel, Switzerland) · 2025Article
- HDF-Net: Hierarchical Dual-Branch Feature Extraction Fusion Network for Infrared and Visible Image Fusion.Sensors (Basel, Switzerland) · 2025Article
- Hybrid Deep Learning Framework for Continuous User Authentication Based on Smartphone Sensors.Sensors (Basel, Switzerland) · 2025Article
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- An enhanced image restoration using deep learning and transformer based contextual optimization algorithm.Scientific reports · 2025Article
- Development and Validation of a Multi-Task Artificial Intelligence-Assisted System for Small Bowel Capsule Endoscopy.International journal of general medicine · 2025Article
- UAV multi-source data fusion with super-resolution for accurate soybean leaf area index estimation.Frontiers in plant science · 2025Article
- MIMO-Uformer: A Transformer-Based Image Deblurring Network for Vehicle Surveillance Scenarios.Journal of imaging · 2024Article
- Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms.Sensors (Basel, Switzerland) · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore a better version of an image given in a low-quality format. In this study, the efficiency of ViT in image restoration is studied extensively. The ViT architectures are classified for every task of image restoration. Seven image restoration tasks are considered: Image Super-Resolution, Image Denoising, General Image Enhancement, JPEG Compression Artifact Reduction, Image Deblurring, Removing Adverse Weather Conditions, and Image Dehazing. The outcomes, the advantages, the limitations, and the possible areas for future research are detailed. Overall, it is noted that incorporating ViT in the new architectures for image restoration is becoming a rule. This is due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input. Nevertheless, some drawbacks exist, such as the need for more data to show the benefits of ViT over CNN, the increased computational cost due to the complexity of the self-attention block, a more challenging training process, and the lack of interpretability. These drawbacks represent the future research direction that should be targeted to increase the efficiency of ViT in the image restoration domain.
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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.