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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2023 IEEE International Conference on Image Processing (ICIP) S. 3568 - 3572
Hauptverfasser: Lopez-Tapia, Santiago, Wang, Xijun, Katsaggelos, Aggelos K.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 08.10.2023
Schlagworte:
Atmospheric modeling
atmospheric turbulence
deep learning
Degradation
Feature extraction
Nonlinear distortion
restoration
Robustness
Stability analysis
Video
Video sequences
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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