SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 3126–3150 of 3874 papers

TitleStatusHype
Super-Resolution for Practical Automated Plant Disease Diagnosis System—0
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows—0
FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization—0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
Sub-frame Appearance and 6D Pose Estimation of Fast Moving ObjectsCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Joint Spatial and Angular Super-Resolution from a Single Image—0
Self-Enhanced Convolutional Network for Facial Video Hallucination—0
PAG-Net: Progressive Attention Guided Depth Super-resolution Network—0
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss—0
Super-resolved Localisation without Identifying LoS/NLoS Paths—0
MetH: A family of high-resolution and variable-shape image challengesCode0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Joint Super-Resolution and Alignment of Tiny FacesCode0
Frequency Separation for Real-World Super-ResolutionCode0
Fine-Grained Neural Architecture Search—0
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and ResultsCode0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
Towards the Automation of Deep Image Prior—0
Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair—0
Neutron Ghost Imaging—0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
Degrees of freedom for off-the-grid sparse estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified