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 1276–1300 of 3874 papers

TitleStatusHype
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