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 2621–2630 of 3874 papers

TitleStatusHype
Underwater litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network—0
BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention—0
Perceptual Fairness in Image Restoration—0
Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data—0
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds—0
Perceptually-inspired super-resolution of compressed videos—0
Perceptually Optimized Super Resolution—0
Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets—0
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression—0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified