Department of Mathematics
Deblurring and sparse unmixing for hyperspectral images
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.
Alternating direction methods, deblurring, hyperspectral imaging, linear spectral unmixing, total variation (TV)
Source Publication Title
IEEE Transactions on Geoscience and Remote Sensing
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Zhao, Xi-Le, Fan Wang, Ting-Zhu Huang, Michael K. Ng, and Robert J. Plemmons. "Deblurring and sparse unmixing for hyperspectral images." IEEE Transactions on Geoscience and Remote Sensing 51.7 (2013): 4045-4058.