Rank-1 tensor decomposition for hyperspectral image denoising with nonlocal low-rank regularization

Jize Xue, Yongqiang Zhao

科研成果: 书/报告/会议事项章节会议稿件同行评审

15 引用 (Scopus)

摘要

In hyperspectral imagery denoising, rank-1 tensor decomposition (R1TD) model can utilize the spatial and spectral information jointly and reduce the noise efficiently. It is difficult to estimate the rank of hyperspectral imagery accurately, and the rank uncertainty will make the R1TD denoising algorithm inefficient. The nonlocal similar patches have lower rank than image, it can be used in rank-1 tensor decomposition process instead of explicitly estimating rank parameters. In this work, a nonlocal low-rank regularization is introduced to avoid the rank uncertainty to influence denoising performance. Then an alternating direction method of multipliers (ADMM) optimization technique is designed to solve the minimum problem. Compared with the state of art methods, proposed algorithm significantly improves the hyperspectral imagery quality both in visual inspection and image quality indices.

源语言英语
主期刊名Proceedings - 2017 International Conference on Machine Vision and Information Technology, CMVIT 2017
出版商Institute of Electrical and Electronics Engineers Inc.
40-45
页数6
ISBN(电子版)9781509049936
DOI
出版状态已出版 - 14 3月 2017
活动2017 International Conference on Machine Vision and Information Technology, CMVIT 2017 - Singapore, 新加坡
期限: 17 2月 201719 2月 2017

出版系列

姓名Proceedings - 2017 International Conference on Machine Vision and Information Technology, CMVIT 2017

会议

会议2017 International Conference on Machine Vision and Information Technology, CMVIT 2017
国家/地区新加坡
Singapore
时期17/02/1719/02/17

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