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 2931–2940 of 3874 papers

TitleStatusHype
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation—0
Efficient and Phase-aware Video Super-resolution for Cardiac MRI—0
Single Image Super-Resolution via Residual Neuron Attention Networks—0
Iterative Network for Image Super-ResolutionCode1
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial NetworkCode1
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral ImageryCode1
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation—0
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution—0
MedSRGAN: medical images super-resolution using generative adversarial networksCode1
A Generative Model for Generic Light Field Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified