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 3051–3100 of 3874 papers

TitleStatusHype
Semantic-Aware Depth Super-Resolution in Outdoor Scenes—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Application of Video-to-Video Translation Networks to Computational Fluid Dynamics—0
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation—0
A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data—0
Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds—0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing—0
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network—0
A parametric non-negative coupled canonical polyadic decomposition algorithm for hyperspectral super-resolution—0
Semi-Supervised Super-Resolution—0
4KAgent: Agentic Any Image to 4K Super-Resolution—0
Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect—0
A Papier-Mâché Approach to Learning 3D Surface Generation—0
Separation-Free Spectral Super-Resolution via Convex Optimization—0
Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach—0
Sequence Matters: Harnessing Video Models in 3D Super-Resolution—0
VEnhancer: Generative Space-Time Enhancement for Video Generation—0
Seven ways to improve example-based single image super resolution—0
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network—0
Sex-Classification from Cell-Phones Periocular Iris Images—0
SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising—0
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement—0
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal—0
Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network—0
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips—0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
A Novel Dual Dense Connection Network for Video Super-resolution—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Signal reconstruction via operator guiding—0
SIGNET: Efficient Neural Representation for Light Fields—0
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution—0
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution—0
Simple and Efficient Unpaired Real-world Super-Resolution using Image Statistics—0
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps—0
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models—0
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data—0
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction—0
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics—0
An Operator Theory for Analyzing the Resolution of Multi-illumination Imaging Modalities—0
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network—0
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding—0
Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning—0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera—0
Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks—0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified