Hyperspectral image denoising with enhanced multivariance product representation

Evrim Korkmaz Özay*, Burcu Tunga

*Bu çalışma için yazışmadan sorumlu yazar

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1 Atıf (Scopus)


Hyperspectral images are used in many different fields due to their ability to capture wide areas and rich spectrality. However, applications on hyperspectral image (HSI) are affected or limited by various types of noise. Therefore, denoising is an important pre-processing technique for HSI analysis. Tensor decomposition-based denoising algorithms are frequently used due to constraints of traditional two-dimensional methods. An alternative tensor decomposition, enhanced multivariance product representation (EMPR) has been derived from high-dimensional model representation (HDMR) for multivariate functions and discretized for tensor-type data sets. In this study, EMPR-based denoising method is proposed for HSI denoising. EMPR is a decomposition method which is easy to compute and does not include a rank problem that exists in the other tensor decomposition methods. The performance of EMPR-based denoising is evaluated by means of simulated and real experiments on different HSI data sets which include different types of noise. The obtained results are compared with the state-of-the-art tensor-based methods.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1127-1133
Sayfa sayısı7
DergiSignal, Image and Video Processing
Basın numarası4
Yayın durumuYayınlandı - Haz 2022

Bibliyografik not

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

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