Online deconvolution for pushbroom hyperspectral imaging systems

被引：0

作者：

Song, Yingying ^{[1
]}

Djermoune, El-Hadi ^{[1
]}

Chen, Jie ^{[2
]}

Richard, Cedric ^{[3
]}

Brie, David ^{[1
]}

机构：

[1] Univ Lorraine, CRAN, CNRS, Vandoeuvre Les Nancy, France

[2] Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China

[3] Univ Cote Azur, CNRS, OCA, Nice, France

来源：

2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP) | 2017年

关键词：

POSITIVE DECONVOLUTION; RESTORATION; ALGORITHM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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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页数：5

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