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 3441–3450 of 3874 papers

TitleStatusHype
Machine learning for reconstruction of polarity inversion lines from solar filamentsCode0
A Deep Journey into Super-resolution: A surveyCode0
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachCode0
Deep Fourier Up-SamplingCode0
Super-Resolution Reconstruction from Bayer-Pattern Spike StreamsCode0
Patch-based Progressive 3D Point Set UpsamplingCode0
Patch-Based Stochastic Attention for Image EditingCode0
Patch-Ordering as a Regularization for Inverse Problems in Image ProcessingCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and BaselineCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified