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 1781–1790 of 3874 papers

TitleStatusHype
Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection—0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing—0
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution—0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution—0
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution—0
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges—0
Conditional Neural Video Coding with Spatial-Temporal Super-Resolution—0
Combined Generative and Predictive Modeling for Speech Super-resolution—0
Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New MethodCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified