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 1801–1810 of 3874 papers

TitleStatusHype
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision—0
Joint Flow And Feature Refinement Using Attention For Video Restoration—0
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution—0
Image Super-Resolution With Deep Variational Autoencoders—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets—0
Image-to-image domain adaptation for vehicle re-identification—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified