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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1575 of 3874 papers

TitleStatusHype
Improving Scene Text Image Super-resolution via Dual Prior Modulation NetworkCode1
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and DeblurringCode1
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operatorCode1
Guided Depth Map Super-resolution: A SurveyCode1
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales—0
Kernelized Back-Projection Networks for Blind Super Resolution—0
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function SpaceCode1
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation—0
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-ResolutionCode1
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods—0
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution—0
Hyperspectral Image Super Resolution with Real Unaligned RGB GuidanceCode1
Variational Mixture of HyperGenerators for Learning Distributions Over FunctionsCode0
I^2SB: Image-to-Image Schrödinger BridgeCode2
Towards Geospatial Foundation Models via Continual PretrainingCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware TransformerCode1
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
Benchmarking Probabilistic Deep Learning Methods for License Plate RecognitionCode0
Energy-Inspired Self-Supervised Pretraining for Vision Models—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