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.
引用
收藏
页数：5
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