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 2011–2020 of 3874 papers

TitleStatusHype
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
Learning a Mixture of Deep Networks for Single Image Super-Resolution—0
Wider Channel Attention Network for Remote Sensing Image Super-resolution—0
Towards Robust Drone Vision in the Wild—0
Learning-Based and Quality Preserving Super-Resolution of Noisy Images—0
Learning based Deep Disentangling Light Field Reconstruction and Disparity Estimation Application—0
Learning-based Framework for US Signals Super-resolution—0
Learning-Based Quality Assessment for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified