Table of Contents
Advances in Optics
Volume 2016 (2016), Article ID 6492197, 7 pages
Research Article

Improved TV Algorithm Based on Adaptive Multiplier for Interference Hyperspectral Image Decomposition

1Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
2College of Automation, Harbin Engineering University, Harbin 150001, China
3College of Computer Science, Xi’an Shiyou University, Xi’an 710065, China

Received 11 February 2016; Revised 23 April 2016; Accepted 28 April 2016

Academic Editor: Zhaolin Lu

Copyright © 2016 Jia Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Interference Hyperspectral Images (IHI) data acquired by Interference Hyperspectral Imaging Spectrometer exhibit many vertical interference stripes. The above characteristics will affect the application of dictionary learning and compressed sensing theory used on IHI data. According to the special characteristics of IHI data, many algorithms are proposed to separate the interference stripes layers and the background layers of IHI data in 2015, but the interference stripes layers are still not clean enough and the ideal background layers without interference stripes are also difficult to be obtained. In this paper, an improved total variation (TV) algorithm based on adaptive multiplier is proposed for IHI data decomposition. The value of the Lagrange multiplier is adaptive according to the unidirectional characteristics of IHI data. The proposed algorithm is used on Large Spatially Modulated Interference Spectral (LSMIS) images and is proved to provide better experimental results than the current algorithms both visually and quantitatively.