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 3081–3090 of 3874 papers

TitleStatusHype
Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Faces—0
Feature-Driven Super-Resolution for Object Detection—0
When to Use Convolutional Neural Networks for Inverse Problems—0
Super Resolution for Root ImagingCode0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth—0
Learning regularization and intensity-gradient-based fidelity for single image super resolution—0
Across-scale Process Similarity based Interpolation for Image Super-Resolution—0
Towards a Computer Vision Particle Flow—0
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified