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 2581–2590 of 3874 papers

TitleStatusHype
Self-Supervised Adaptation for Video Super-ResolutionCode1
Prediction-assistant Frame Super-Resolution for Video Streaming—0
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar—0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
A learning-based view extrapolation method for axial super-resolution—0
3D Human Pose, Shape and Texture from Low-Resolution Images and VideosCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified