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 3001–3010 of 3874 papers

TitleStatusHype
Using Super-Resolution Imaging for Recognition of Low-Resolution Blurred License Plates: A Comparative Study of Real-ESRGAN, A-ESRGAN, and StarSRGAN—0
A Reverse Hierarchy Model for Predicting Eye Fixations—0
V2X Sidelink Positioning in FR1: From Ray-Tracing and Channel Estimation to Bayesian Tracking—0
A Restoration Network as an Implicit Prior—0
A Resolution Enhancement Plug-in for Deformable Registration of Medical Images—0
A recommender system to restore images with impulse noise—0
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution—0
Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study—0
A Real Time Super Resolution Accelerator with Tilted Layer Fusion—0
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified