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 3321–3330 of 3874 papers

TitleStatusHype
Multi-scale deep neural networks for real image super-resolutionCode0
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators—0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Feature Forwarding for Efficient Single Image DehazingCode0
Process of image super-resolution—0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
A Deep Journey into Super-resolution: A surveyCode0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified