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 2861–2870 of 3874 papers

TitleStatusHype
Progressive Image Super-Resolution via Neural Differential Equation—0
Regularization via deep generative models: an analysis point of view—0
GhostSR: Learning Ghost Features for Efficient Image Super-ResolutionCode0
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices—0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees—0
Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars—0
Single Image Super-Resolution—0
More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination—0
VHS to HDTV Video Translation using Multi-task Adversarial Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified