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 2631–2640 of 3874 papers

TitleStatusHype
Back-Projection Pipeline—0
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-ResolutionCode1
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstructionCode1
Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering—0
Progressive Image Super-Resolution via Neural Differential Equation—0
GhostSR: Learning Ghost Features for Efficient Image Super-ResolutionCode0
Regularization via deep generative models: an analysis point of view—0
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices—0
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous DatasetsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified