NDLI: Reweighted laplace prior based hyperspectral compressive sensing for unknown sparsity

Content Provider	IEEE Xplore Digital Library
Author	Lei Zhang Wei Wei Yanning Zhang Chunna Tian Fei Li
Copyright Year	2015
Description	Author affiliation: Sch. of Electron. Eng., Xidian Univ., Xi'an, China (Chunna Tian) \|\| Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi'an, China (Lei Zhang; Wei Wei; Yanning Zhang; Fei Li)
Abstract	Compressive sensing(CS) has been exploited for hype-spectral image(HSI) compression in recent years. Though it can greatly reduce the costs of computation and storage, the reconstruction of HSI from a few linear measurements is challenging. The underlying sparsity of HSI is crucial to improve the reconstruction accuracy. However, the sparsity of HSI is unknown in reality and varied with different noise, which makes the sparsity estimation difficult. To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study. First, the reweighted Laplace prior is proposed to model the distribution of sparsity in HSI. Second, the latent variable Bayes model is employed to learn the optimal configuration of the reweighted Laplace prior from the measurements. The model unifies signal recovery, prior learning and noise estimation into a variational framework to infer the parameters automatically. The learned sparsity prior can represent the underlying structure of the sparse signal very well and is adaptive to the unknown noise, which improves the reconstruction accuracy of HSI. The experimental results on three hyperspectral datasets demonstrate the proposed method outperforms several state-of-the-art hyperspectral CS methods on the reconstruction accuracy.
Starting Page	2274
Ending Page	2281
File Size	487682
Page Count	8
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298840
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Yttrium Noise Estimation Hyperspectral imaging Image reconstruction Sparse matrices Optimization
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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