- 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.
Download
- full text manuscript: PDF (3.2MB)
- source code: TAR/GZ
Supplementary Materials
Available datasets :
- bateau 5m: video, frames
- bateau 10m: video, frames
- bateau R: video, frames
- lena 5m: video, frames
- lena 10m: video, frames
- lena R: video, frames
- poeme 5m: video, frames
- poeme 10m: video, frames
- poeme R: video, frames
- points 5m: video, frames
- points 10m: video, frames
- points R: video, frames
History
- Note from the editor: the manuscript of the article was modified on 2022-01-01 to include information about its editors. The original version of the manuscript is available here.