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 1811–1820 of 3874 papers

TitleStatusHype
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and RestorationCode2
CMGAN: Conformer-Based Metric-GAN for Monaural Speech EnhancementCode2
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
Gemino: Practical and Robust Neural Compression for Video Conferencing—0
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention—0
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified