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 2781–2790 of 3874 papers

TitleStatusHype
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach—0
Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes—0
SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time—0
Supervised Deep Kriging for Single-Image Super-Resolution—0
Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation—0
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface—0
Surface Geometry Processing: An Efficient Normal-based Detail Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified