@inproceedings{912c8a187960422c82560af711a4a0a7,
title = "Hyperspectral restoration employing low rank and 3D total variation regularization",
abstract = "This paper presents a novel mixed-noise removal method by employing low rank constraint and 3-D total variation regularization for hyperspectral image (HSI) restoration. The main idea of the proposed method is based on the assumption that the spectra in HSI lie in the same low rank subspace and both spatial and spectral domains exhibit the property of piecewise smoothness. The low rank property of HSI is exploited by the nuclear norm, while the spectral-spatial smoothness is explored by 3D total variation (3DTV) which is defined as a combination of 2-D spatial TV and 1-D spectral TV of the HSI cube. Finally, the proposed restoration model is effectively solved by alternating direction method of multipliers (ADMM). Experimental results on simulated HSI dataset validate the superior performance of the proposed method.",
keywords = "3D total variation, ADMM, Hyperspectral image, Low rank property, Restoration",
author = "Le Sun and Yuhui Zheng and Byeungwoo Jeon",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 ; Conference date: 23-12-2016 Through 25-12-2016",
year = "2017",
month = jun,
day = "15",
doi = "10.1109/PIC.2016.7949519",
language = "English",
series = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "326--329",
editor = "Yinglin Wang and Yaoru Sun",
booktitle = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",
}