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 2681–2690 of 3874 papers

TitleStatusHype
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution—0
Attention-based Multi-Reference Learning for Image Super-ResolutionCode0
Super-Resolution Appearance Transfer for 4D Human Performances—0
The End Restraint Method for Mechanically Perturbing Nucleic Acids in silico—0
Multi-Attributed and Structured Text-to-Face Synthesis—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
Temporal Kernel Consistency for Blind Video Super-Resolution—0
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
Seirios: Leveraging Multiple Channels for LoRaWAN Indoor and Outdoor Localization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified