@inproceedings{71498d3d2272490b9e4958dd715a1bcd,
title = "Unified functional framework for restoration of image sequences degraded by atmospheric turbulence",
abstract = "We propose a unified functional to address the restoration of turbulence-degraded images. This functional quantifies the association between a given image sequence and a candidate latent image restoration. Minimizing the functional using the alternating direction method of multipliers (ADMM) and Moreau proximity mapping leads to a general algorithmic flow. We show that various known algorithms can be derived as special cases of the general approach. Furthermore, we show that building-blocks used in turbulence recovery algorithms, such as optical flow estimation and blind deblurring, are called for by the general model. The main contribution of this work is the establishment of a unified theoretical framework for the restoration of turbulence-degraded images. It leads to novel turbulence recovery algorithms as well as to better understanding of known ones.",
author = "Naftali Zon and Nahum Kiryati",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 11th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMVCPR 2017 ; Conference date: 30-10-2017 Through 01-11-2017",
year = "2018",
doi = "10.1007/978-3-319-78199-0_14",
language = "אנגלית",
isbn = "9783319781983",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "205--219",
editor = "Marcello Pelillo and Edwin Hancock",
booktitle = "Energy Minimization Methods in Computer Vision and Pattern Recognition - 11th International Conference, EMMCVPR 2017, Revised Selected Papers",
address = "גרמניה",
}