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Fast video super-resolution using artificial neural networks

https://doi.org/10.1109/csndsp.2012.6292646
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.rse.2005.02.006openalex-snapshot
Super-resolution land cover mapping using a Markov random field based approach
inferredcites →https://doi.org/10.1016/j.sigpro.2009.09.002openalex-snapshot
A super-resolution reconstruction algorithm for surveillance images
inferredcites →https://doi.org/10.1016/j.sigpro.2010.12.016openalex-snapshot
Video super-resolution reconstruction using a mobile search strategy and adaptive patch size
inferredcites →https://doi.org/10.1109/tip.2007.896644openalex-snapshot
Image Superresolution Using Support Vector Regression
inferredcites →https://doi.org/10.1109/tip.2008.2008067openalex-snapshot
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
inferredcites →https://doi.org/10.1109/tip.2009.2023703openalex-snapshot
Super-Resolution Without Explicit Subpixel Motion Estimation
inferredcites →https://doi.org/10.1109/tmi.2005.861705openalex-snapshot
Super-resolution in PET imaging
inferredcites →https://doi.org/10.1109/tpami.2005.85openalex-snapshot
Space-time super-resolution

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4679125openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-319-66709-6_17openalex-snapshot
End-to-End Learning of Video Super-Resolution with Motion Compensation
inferred← citeshttps://doi.org/10.1007/s11760-018-1346-1openalex-snapshot
Multiframe image superresolution based on cepstral analysis
inferred← citeshttps://doi.org/10.1016/j.dsp.2018.07.005openalex-snapshot
Multimedia super-resolution via deep learning: A survey
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106216openalex-snapshot
Super-resolution network with dynamic cleanup and temporal–spatial attention for compressed videos
inferred← citeshttps://doi.org/10.1016/j.image.2024.117175openalex-snapshot
A ‘deep’ review of video super-resolution
inferred← citeshttps://doi.org/10.1016/j.patrec.2021.10.030openalex-snapshot
GEVE: A generative adversarial network for extremely dark image/video enhancement
inferred← citeshttps://doi.org/10.1109/icip.2016.7532538openalex-snapshot
Super-resolution of compressed videos using convolutional neural networks
inferred← citeshttps://doi.org/10.1109/icoei.2018.8553719openalex-snapshot
Video Super Resolution with Generative Adversarial Network
inferred← citeshttps://doi.org/10.1109/tci.2016.2532323openalex-snapshot
Video Super-Resolution With Convolutional Neural Networks
inferred← citeshttps://doi.org/10.11591/ijeecs.v8.i1.pp43-58openalex-snapshot
An Adaptive Scheme to Achieve Fine Grained Video Scaling
inferred← citeshttps://doi.org/10.11834/jig.220130openalex-snapshot
Deep learning based video-related super-resolution technique： a survey
inferred← citeshttps://doi.org/10.1561/9781680839739openalex-snapshot
Deep Learning for Image/Video Restoration and Super-resolution
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