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 2571–2580 of 3874 papers

TitleStatusHype
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence—0
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement—0
Over-the-Air Time-Frequency Synchronization in Distributed ISAC Systems—0
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model—0
Padding-free Convolution based on Preservation of Differential Characteristics of Kernels—0
PAG-Net: Progressive Attention Guided Depth Super-resolution Network—0
Capsule GAN for Prostate MRI Super-Resolution—0
Can SAM Boost Video Super-Resolution?—0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified