Abstract:
This paper introduces a framework based on the LMS algorithm for sequential deconvolution of hyperspectral images acquired by industrial pushbroom imaging systems. Consid...Show MoreMetadata
Abstract:
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.
Published in: 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Date of Conference: 10-13 December 2017
Date Added to IEEE Xplore: 12 March 2018
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