Published December 10, 2017
| Version v1
Conference paper
Online deconvolution for pushbroom hyperspectral imaging systems
- Others:
- Centre de Recherche en Automatique de Nancy (CRAN) ; Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
- Northwestern Polytechnical University [Xi'an] (NPU)
- Joseph Louis LAGRANGE (LAGRANGE) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)
- IEEE
Description
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.
Abstract
International audience
Additional details
- URL
- https://hal.science/hal-01493901
- URN
- urn:oai:HAL:hal-01493901v2
- Origin repository
- UNICA