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 2691–2700 of 3874 papers

TitleStatusHype
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Hierarchical Residual Attention Network for Single Image Super-ResolutionCode1
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis—0
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias ClassificationCode0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
BasicVSR: The Search for Essential Components in Video Super-Resolution and BeyondCode3
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Learning Spatial Attention for Face Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified