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 3801–3825 of 3874 papers

TitleStatusHype
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Global graph features unveiled by unsupervised geometric deep learning—0
Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations—0
Global-Local Face Upsampling Network—0
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution—0
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials—0
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
G-PCC Post-Processing Using Fractional Super-Resolution—0
Gradient-Free Adversarial Purification with Diffusion Models—0
DuCos: Duality Constrained Depth Super-Resolution via Foundation Model—0
DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation—0
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds—0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution—0
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Gridless Parameter Estimation in Partly Calibrated Rectangular Arrays—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified