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 711–720 of 3874 papers

TitleStatusHype
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
Bayesian Conditioned Diffusion Models for Inverse Problems—0
GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-ResolutionCode1
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
One-Step Effective Diffusion Network for Real-World Image Super-ResolutionCode4
Image Neural Field Diffusion Models—0
Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified