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 731–740 of 3874 papers

TitleStatusHype
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial LearningCode1
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model InferenceCode1
Implicit Neural Image StitchingCode1
Action Matching: Learning Stochastic Dynamics from SamplesCode1
Improving Super-Resolution Performance using Meta-Attention LayersCode1
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object DetectionCode1
Asymmetric CNN for image super-resolutionCode1
Instant recovery of shape from spectrum via latent space connectionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified