Study of the Principal Component Analysis Method for the Correction of Images Degraded by Turbulence
Tristan Dagobert, Yohann Tendero, Stéphane Landeau
→ BibTeX
@article{ipol.2018.47,
    title   = {{Study of the Principal Component Analysis Method for the 
    Correction of Images Degraded by Turbulence}},
    author  = {Dagobert, Tristan and Tendero, Yohann and Landeau, Stéphane},
    journal = {{Image Processing On Line}},
    volume  = {8},
    pages   = {388--407},
    year    = {2018},
    doi     = {10.5201/ipol.2018.47},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2018.47}}
published
2018-11-23
reference
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

Communicated by Enric Meinhardt-Llopis
Demo edited by Tristan Dagobert

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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Supplementary Materials

Available datasets :