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 3526–3550 of 3874 papers

TitleStatusHype
Evaluation of Machine-generated Biomedical Images via A Tally-based Similarity Measure—0
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods—0
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset—0
Event-based Video Super-Resolution via State Space Models—0
Enhance the Image: Super Resolution using Artificial Intelligence in MRI—0
Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing—0
Event Signal Filtering via Probability Flux Estimation—0
Enhanced Signal Recovery via Sparsity Inducing Image Priors—0
Event-Stream Super Resolution using Sigma-Delta Neural Network—0
Event Stream Super-Resolution via Spatiotemporal Constraint Learning—0
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR—0
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Enhanced Image Reconstruction From Quarter Sampling Measurements Using An Adapted Very Deep Super Resolution Network—0
Example-Based Modeling of Facial Texture From Deficient Data—0
Example-based super-resolution for point-cloud video—0
Adversarial Image Alignment and Interpolation—0
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution—0
Expansion microscopy reveals neural circuit organization in genetic animal models—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images—0
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified