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Proceedings Paper

Multiscale statistical image destriping algorithm
Author(s): Vincent Martin; Arnaud Kelbert
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Paper Abstract

This paper presents a multi-scale framework for image destriping algorithms, allowing estimating image normalization coefficients adapted to stripe artifacts covering a large range of spatial frequencies. This algorithm can address destriping of push- and whisk-broom satellite images, which often present residual striping patterns along the scanning direction. The proposed method is however generic and can be applied to any image including unidirectional structured noise, e.g. vertically or horizontally. Only a single spectral image channel is required, whereas extension to multi-channel imagery is straightforward. It is an unsupervised method, which is essential to process any acquisition in an operational ground segment. This paper combines the proposed framework with a MAP-estimation-based state-of-the art destriping algorithm and presents applications to real satellite imagery.

Paper Details

Date Published: 15 October 2015
PDF: 9 pages
Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430B (15 October 2015); doi: 10.1117/12.2195002
Show Author Affiliations
Vincent Martin, Ctr. National d'Études Spatiales (France)
Arnaud Kelbert, Thales Services (France)

Published in SPIE Proceedings Vol. 9643:
Image and Signal Processing for Remote Sensing XXI
Lorenzo Bruzzone, Editor(s)

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