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 651–660 of 3874 papers

TitleStatusHype
Learning Graph Regularisation for Guided Super-ResolutionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-ResolutionCode1
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolutionCode1
Increasing the accuracy and resolution of precipitation forecasts using deep generative modelsCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect ArrayCode1
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolutionCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified