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 2841–2850 of 3874 papers

TitleStatusHype
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution—0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Mobile Computational Photography: A Tour—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images—0
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance imagesCode0
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified