Variational Deep Atmospheric Turbulence Correction for Video

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 i...

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Bibliographic Details
Published in:2023 IEEE International Conference on Image Processing (ICIP) pp. 3568 - 3572
Main Authors: Lopez-Tapia, Santiago, Wang, Xijun, Katsaggelos, Aggelos K.
Format: Conference Proceeding
Language:English
Published: IEEE 08.10.2023
Subjects:
Atmospheric modeling
atmospheric turbulence
deep learning
Degradation
Feature extraction
Nonlinear distortion
restoration
Robustness
Stability analysis
Video
Video sequences
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Summary: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.
DOI:10.1109/ICIP49359.2023.10222374