Study of the Principal Component Analysis Method for the Correction of Images Degraded by Turbulence

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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https://hal.telecom-paristech.fr/hal-02287990
Contributor : Telecomparis Hal <>
Submitted on : Friday, September 13, 2019 - 5:34:07 PM
Last modification on : Thursday, October 17, 2019 - 12:37:00 PM

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  • HAL Id : hal-02287990, version 1

Citation

Tristan Dagobert, Yohann Tendero, Stephane Landeau. Study of the Principal Component Analysis Method for the Correction of Images Degraded by Turbulence. Image Processing On Line (IPOL), 2018, 8, pp.388-407. ⟨hal-02287990⟩

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