http://dx.doi.org/10.1109/TGRS.2012.2227764">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Deblurring and sparse unmixing for hyperspectral images

Language

English

Abstract

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 © 1980-2012 IEEE.

Keywords

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

Publication Date

2013

Source Publication Title

IEEE Transactions on Geoscience and Remote Sensing

Volume

51

Issue

7

Start Page

4045

End Page

4058

Publisher

Institute of Electrical and Electronics Engineers

ISSN (print)

01962892

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