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 2601–2610 of 3874 papers

TitleStatusHype
Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different ResolutionsCode1
Learning for Unconstrained Space-Time Video Super-Resolution—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Provable Compressed Sensing with Generative Priors via Langevin Dynamics—0
Deep Unrolled Network for Video Super-Resolution—0
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
SRDTI: Deep learning-based super-resolution for diffusion tensor MRICode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified