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 2071–2080 of 3874 papers

TitleStatusHype
Efficient Super Resolution Using Binarized Neural Network—0
EfficientTempNet: Temporal Super-Resolution of Radar Rainfall—0
Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization—0
Efficient Video Super-Resolution for Real-time Rendering with Decoupled G-buffer Guidance—0
EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction—0
eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging—0
EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera—0
Electro-diffusive modeling and the role of spine geometry on action potential propagation in neurons—0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Embedding Similarity Guided License Plate Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified