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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3701–3710 of 3874 papers

TitleStatusHype
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific SimulationsCode0
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
An FCNN-Based Super-Resolution mmWave Radar Framework for Contactless Musical Instrument InterfaceCode0
Adaptation of the super resolution SOTA for Art Restoration in camera capture imagesCode0
Regularized Training of Intermediate Layers for Generative Models for Inverse ProblemsCode0
A New Multi-Picture Architecture for Learned Video Deinterlacing and Demosaicing with Parallel Deformable Convolution and Self-Attention BlocksCode0
REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI SegmentationCode0
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learningCode0
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR ApplicationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified