[IPOL announce] new article: Study of the Principal Component Analysis Method for the Correction of Images Degraded by Turbulence

[announcements about the IPOL journal]

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.