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 3521–3530 of 3874 papers

TitleStatusHype
Evaluating Detection Thresholds: The Impact of False Positives and Negatives on Super-Resolution Ultrasound Localization Microscopy—0
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models—0
Task-Driven Super Resolution: Object Detection in Low-resolution Images—0
Evaluating the Adversarial Robustness for Fourier Neural Operators—0
Evaluating the Generalization Ability of Super-Resolution Networks—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified