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 1251–1300 of 3874 papers

TitleStatusHype
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
Deep Learning Techniques for Super-Resolution in Video Games—0
Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis—0
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging—0
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
Deep learning in ultrasound imaging—0
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
Deep Learning for Super-resolution Ultrasound Imaging with Spatiotemporal Data—0
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales—0
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator—0
Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks—0
Functional Neural Networks for Parametric Image Restoration Problems—0
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images—0
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy—0
Deep Learning for Inverse Problems: Bounds and Regularizers—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
Bayesian Conditioned Diffusion Models for Inverse Problems—0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy—0
A learning-based view extrapolation method for axial super-resolution—0
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy—0
Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems—0
Supervised Image Translation from Visible to Infrared Domain for Object Detection—0
From General to Specific: Online Updating for Blind Super-Resolution—0
Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe—0
Deep learning-based super-resolution in coherent imaging systems—0
Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution—0
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring—0
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution—0
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified