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 2791–2800 of 3874 papers

TitleStatusHype
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—0
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles—0
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
Real-time, low-cost multi-person 3D pose estimation—0
Real-Time Neural-Enhancement for Online Cloud Gaming—0
Real-Time Neural Video Recovery and Enhancement on Mobile Devices—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Real-Time Super-Resolution for Real-World Images on Mobile Devices—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified