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 3581–3590 of 3874 papers

TitleStatusHype
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy—0
Fast and Accurate Single Image Super-Resolution via Information Distillation NetworkCode0
SUNLayer: Stable denoising with generative networks—0
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual NetworkCode0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
Example-based super-resolution for point-cloud video—0
Maintaining Natural Image Statistics with the Contextual LossCode0
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural networkCode0
Deep Back-Projection Networks For Super-ResolutionCode0
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected NetworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified