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 2171–2180 of 3874 papers

TitleStatusHype
Kernelized Back-Projection Networks for Blind Super Resolution—0
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
Variational Mixture of HyperGenerators for Learning Distributions Over FunctionsCode0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
High-Resolution GAN Inversion for Degraded Images in Large Diverse DatasetsCode0
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal—0
A statistically constrained internal method for single image super-resolution—0
An Operator Theory for Analyzing the Resolution of Multi-illumination Imaging Modalities—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified