Video Variational Deep Atmospheric Turbulence Correction

This paper presents a novel variational deep-learning approach for video atmospheric turbulence correction. We modify and tailor the Nonlinear Activation Free Network (NAFNet) architecture for video restoration, introducing a new transformer-based channel attention mechanism to exploit long-range hi...

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Vydané v:	IEEE access Ročník 12; s. 127368 - 127379
Hlavní autori:	Lopez-Tapia, Santiago, Wang, Xijun, Katsaggelos, Aggelos K.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Atmospheric correction Atmospheric modeling Atmospheric turbulence Deep learning Degradation Feature extraction Image enhancement Image restoration Noise prediction Nonlinear distortion Training video restoration
ISSN:	2169-3536, 2169-3536
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