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 1501–1525 of 3874 papers

TitleStatusHype
Single-Layer Learnable Activation for Implicit Neural Representation (SL^2A-INR)—0
Super Resolution On Global Weather Forecasts—0
NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution—0
Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution—0
Adversarial Deep-Unfolding Network for MA-XRF Super-Resolution on Old Master Paintings Using Minimal Training Data—0
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC—0
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems—0
Test-time Training for Hyperspectral Image Super-resolution—0
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks—0
CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer—0
Three-Dimensional, Multimodal Synchrotron Data for Machine Learning ApplicationsCode0
Lightweight single-image super-resolution network based on dual paths—0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Single-snapshot machine learning for super-resolution of turbulence—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Use of triplet loss for facial restoration in low-resolution images—0
Enhancing digital core image resolution using optimal upscaling algorithm: with application to paired SEM images—0
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization ProblemCode0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
Solving Video Inverse Problems Using Image Diffusion Models—0
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified