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 2501–2550 of 3874 papers

TitleStatusHype
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network—0
Self Super-Resolution for Magnetic Resonance Images using Deep Networks—0
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI—0
Self-Supervised Burst Super-Resolution—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model—0
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks—0
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy—0
Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging—0
Self-Supervised Super-Resolution Approach for Isotropic Reconstruction of 3D Electron Microscopy Images from Anisotropic Acquisition—0
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites—0
Self-Tuned Deep Super Resolution—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
Semantic-Aware Depth Super-Resolution in Outdoor Scenes—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation—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
Semi-Supervised Super-Resolution—0
Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect—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
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
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—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
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
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
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses—0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder—0
Single Image Super-Resolution—0
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified