Hyperspectral image denoising with enhanced multivariance product representation

Ozay E. K., Tunga B.

SIGNAL IMAGE AND VIDEO PROCESSING, vol.16, pp.1127-1133, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 16
  • Publication Date: 2022
  • Doi Number: 10.1007/s11760-021-02062-6
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, zbMATH
  • Page Numbers: pp.1127-1133
  • Keywords: Hyperspectral image, Denoising, Tensor decomposition, Enhanced multivariance product representation, DECOMPOSITION, MODEL
  • Istanbul Technical University Affiliated: Yes


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.