Variational Deep Atmospheric Turbulence Correction for Video

Santiago López-Tapia*, Xijun Wang*, Aggelos K. Katsaggelos*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper presents a novel variational deep-learning approach for video atmospheric turbulence correction. We modify and tailor a Nonlinear Activation Free Network to video restoration. By including it in a variational inference framework, we boost the model's performance and stability. This is achieved through conditioning the model on features extracted by a variational autoencoder (VAE). Furthermore, we enhance these features by making the encoder of the VAE include information pertinent to the image formation via a new loss based on the prediction of parameters of the geometrical distortion and the spatially variant blur responsible for the video sequence degradation. Experiments on a comprehensive synthetic video dataset demonstrate the effectiveness and reliability of the proposed method and validate its superiority compared to existing state-of-the-art approaches.

Original languageEnglish (US)
Title of host publication2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings
PublisherIEEE Computer Society
Pages3568-3572
Number of pages5
ISBN (Electronic)9781728198354
DOIs
StatePublished - 2023
Event30th IEEE International Conference on Image Processing, ICIP 2023 - Kuala Lumpur, Malaysia
Duration: Oct 8 2023Oct 11 2023

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference30th IEEE International Conference on Image Processing, ICIP 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period10/8/2310/11/23

Keywords

  • atmospheric turbulence
  • deep learning
  • restoration
  • Video

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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