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 2061–2070 of 3874 papers

TitleStatusHype
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
Reference-based Video Super-Resolution Using Multi-Camera Video TripletsCode2
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
Neural Vocoder is All You Need for Speech Super-resolutionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-ResolutionCode1
Learning Graph Regularisation for Guided Super-ResolutionCode1
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolutionCode1
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified