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 3101–3150 of 3874 papers

TitleStatusHype
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
Scale-wise Convolution for Image RestorationCode1
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Adaptive Densely Connected Super-Resolution ReconstructionCode0
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal LossCode0
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational ApproachCode0
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks—0
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop—0
HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks—0
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Cross-Resolution Learning for Face RecognitionCode0
Explorable Super ResolutionCode0
High-quality Speech Synthesis Using Super-resolution Mel-Spectrogram—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
Learning to Super Resolve Intensity Images from EventsCode0
Texture Hallucination for Large-Factor Painting Super-Resolution—0
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.Code0
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-ResolversCode0
Learning Likelihoods with Conditional Normalizing FlowsCode0
Learning from Irregularly Sampled Data for Endomicroscopy Super-resolution: A Comparative Study of Sparse and Dense Approaches—0
Quality analysis of DCGAN-generated mammography lesions—0
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices—0
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