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 401–410 of 3874 papers

TitleStatusHype
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion ModelCode1
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
AutoGAN-Distiller: Searching to Compress Generative Adversarial NetworksCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstructionCode1
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified