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 1801–1825 of 3874 papers

TitleStatusHype
Low-Resolution Action Recognition for Tiny Actions Challenge—0
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing ImageryCode2
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances—0
Scaling Laws For Deep Learning Based Image ReconstructionCode0
Deep generative model super-resolves spatially correlated multiregional climate data—0
A heterogeneous group CNN for image super-resolutionCode1
Effective Invertible Arbitrary Image Rescaling—0
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark DatasetCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
CMGAN: Conformer-Based Metric-GAN for Monaural Speech EnhancementCode2
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and RestorationCode2
Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy—0
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System—0
HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face ImagesCode1
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention—0
Gemino: Practical and Robust Neural Compression for Video Conferencing—0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques—0
Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks—0
Text2Light: Zero-Shot Text-Driven HDR Panorama GenerationCode2
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models—0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors—0
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified