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Communication Dans Un Congrès Année : 2017

Online deconvolution for pushbroom hyperspectral imaging systems

Yingying Song
Jie Chen
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Cédric Richard
David Brie

Résumé

This paper introduces a framework based on the LMS algorithm for sequential deconvolution of hyperspectral images acquired by industrial pushbroom imaging systems. Considering a sequential model of image blurring phenomenon, we derive a sliding-block zero-attracting LMS algorithm with spectral regularization. The role of each hyper-parameter is discussed. The performance of the algorithm is evaluated using real hyperspectral data.
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Dates et versions

hal-01493901 , version 1 (22-03-2017)
hal-01493901 , version 2 (05-02-2018)

Identifiants

  • HAL Id : hal-01493901 , version 2

Citer

Yingying Song, El-Hadi Djermoune, Jie Chen, Cédric Richard, David Brie. Online deconvolution for pushbroom hyperspectral imaging systems. 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017, IEEE, Dec 2017, Curaçao, Netherlands Antilles. ⟨hal-01493901v2⟩
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