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 2341–2350 of 3874 papers

TitleStatusHype
Deep generative model super-resolves spatially correlated multiregional climate data—0
Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy—0
3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System—0
Gemino: Practical and Robust Neural Compression for Video Conferencing—0
Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention—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
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified