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 3351–3360 of 3874 papers

TitleStatusHype
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR ApplicationsCode0
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators—0
Multi-scale deep neural networks for real image super-resolutionCode0
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
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Process of image super-resolution—0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
A Deep Journey into Super-resolution: A surveyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified