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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1375 of 3874 papers

TitleStatusHype
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
Model-Based Deep LearningCode1
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting—0
AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices—0
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection—0
A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-ResolutionCode1
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and ReportCode1
Towards Real-Time 4K Image Super-ResolutionCode1
Spatio-Angular Convolutions for Super-resolution in Diffusion MRICode0
Symmetric Uncertainty-Aware Feature Transmission for Depth Super-ResolutionCode1
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models—0
Physics-Informed Ensemble Representation for Light-Field Image Super-ResolutionCode0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
Toward Real-World Light Field Super-ResolutionCode0
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution—0
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution—0
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution LayersCode1
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Learning from Multi-Perception Features for Real-Word Image Super-resolution—0
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth—0
High-Similarity-Pass Attention for Single Image Super-Resolution—0
GenerateCT: Text-Conditional Generation of 3D Chest CT VolumesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified