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 3651–3660 of 3874 papers

TitleStatusHype
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection ModelsCode0
Wavelet-SRNet: A Wavelet-Based CNN for Multi-Scale Face Super Resolution—0
Multi-View Dynamic Shape Refinement Using Local Temporal Integration—0
Learning to Super-Resolve Blurry Face and Text Images—0
Anchored Regression Networks Applied to Age Estimation and Super Resolution—0
Image Super-Resolution Using Dense Skip ConnectionsCode0
Robust Video Super-Resolution With Learned Temporal Dynamics—0
Light field super resolution through controlled micro-shifts of light field sensor—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified