A new article is available in IPOL: https://www.ipol.im/pub/art/2018/47/
Tristan Dagobert, Yohann Tendero, and Stéphane Landeau,
Study of the Principal Component Analysis Method for the Correction of
Images Degraded by Turbulence,
Image Processing On Line, 8 (2018), pp. 388–407.
https://doi.org/10.5201/ipol.2018.47
Abstract
This article analyzes and discusses a well-known paper [D. Li, R.M.
Mersereau and S. Simske, IEEE Letters on Geoscience and Remote Sensing,
3:4 (2007), pp. 340--344] that applies principal component analysis in
order to restore image sequences degraded by atmospheric turbulence. We
propose a variant of this method and its ANSI C implementation. The
proposed variant applies to image sequences acquired with short as well
as long exposure times. Examples of restored images using sequences of
real atmospheric turbulence are presented. The acquisition of a dataset
of image sequences with real atmospheric turbulence is described and the
dataset is made available for download.
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IPOL - Image Processing On Line - http://ipol.im/
contact [email protected] - http://www.ipol.im/meta/contact/
news+feeds twitter @IPOL_journal - http://www.ipol.im/meta/feeds/
announces [email protected] - http://tools.ipol.im/mm/announce/
discussions [email protected] - http://tools.ipol.im/mm/discuss/
