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 3026–3050 of 3874 papers

TitleStatusHype
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Self-Organized Residual Blocks for Image Super-Resolution—0
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel—0
ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior—0
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network—0
Zoom to Learn, Learn to Zoom—0
Self Super-Resolution for Magnetic Resonance Images using Deep Networks—0
Why Are Deep Representations Good Perceptual Quality Features?—0
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI—0
Self-Supervised Burst Super-Resolution—0
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
A Preliminary Exploration Towards General Image Restoration—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
Applying VertexShuffle Toward 360-Degree Video Super-Resolution on Focused-Icosahedral-Mesh—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified