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Open AccessArticle

Variational Destriping in Remote Sensing Imagery: Total Variation with L1 Fidelity

1
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
2
Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA 90095, USA
3
Department of Mathematics, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 300; https://doi.org/10.3390/rs10020300
Received: 3 January 2018 / Revised: 28 January 2018 / Accepted: 12 February 2018 / Published: 15 February 2018
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based on the basic principles of regularization and data fidelity with certain constraints using modern methods in variational optimization, namely, total variation (TV), L 1 fidelity, and the alternating direction method of multipliers (ADMM). The proposed algorithm, TV– L 1 , uses sparsity-promoting energy functionals to achieve two important imaging effects. The TV term maintains boundary sharpness of the content in the underlying clean image, while the L 1 fidelity allows for the equitable removal of stripes without over- or under-penalization, providing a more accurate model of presumably independent sensors with an unspecified and unrestricted bias distribution. A comparison is made between the TV– L 2 model and the proposed TV– L 1 model to exemplify the qualitative efficacy of an L 1 striping penalty. The model makes use of novel minimization splittings and proximal mapping operators, successfully yielding more realistic destriped images in very few iterations. View Full-Text
Keywords: alternating direction method of multipliers (ADMM); image striping restoration; raster scan; sparse optimization; split Bregman; total variation; variational destriping alternating direction method of multipliers (ADMM); image striping restoration; raster scan; sparse optimization; split Bregman; total variation; variational destriping
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MDPI and ACS Style

Yanovsky, I.; Dragomiretskiy, K. Variational Destriping in Remote Sensing Imagery: Total Variation with L1 Fidelity. Remote Sens. 2018, 10, 300.

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