Deblurring and sparse unmixing for hyperspectral images

Xi Le Zhao, Fan Wang, Ting Zhu Huang, Kwok Po Ng*, Robert J. Plemmons

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

146 Citations (Scopus)


The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed from axial optical aberrations in the acquisition system. An alternating direction method is developed to solve the resulting optimization problem efficiently. According to the structure of the TV regularization and sparse unmixing in the model, the convergence of the alternating direction method can be guaranteed. Experimental results are reported to demonstrate the effectiveness of the TV and sparsity model and the efficiency of the proposed numerical scheme, and the method is compared to the recent Sparse Unmixing via variable Splitting Augmented Lagrangian and TV method by Iordache

Original languageEnglish
Article number6423278
Pages (from-to)4045-4058
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number7
Publication statusPublished - Jul 2013

Scopus Subject Areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

User-Defined Keywords

  • Alternating direction methods
  • deblurring
  • hyperspectral imaging
  • linear spectral unmixing
  • total variation (TV)


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