Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Joint sparse and low rank recovery algorithm for compressive hyperspectral imaging

Not Accessible

Your library or personal account may give you access

Abstract

Compressive spectral imaging techniques encode and disperse a hyperspectral image (HSI) to sense its spatial and spectral information with few bidimensional (2D) multiplexed projections. Recovering the original HSI from the 2D projections is carried by traditional compressive sensing-based techniques that exploit the sparsity property of natural HSI as they are represented in a proper orthonormal basis. Nevertheless, HSIs also exhibit a low rank property inasmuch only a few numbers of spectral signatures are present in the images. Specifically, when an HSI is rearranged as a matrix whose columns represent vectorized 2D spatial images in a different wavelength, this matrix is said to be low rank. Therefore, this paper proposes an HSI recovering algorithm from compressed measurements involving a joint sparse and low rank optimization problem, which seeks to jointly minimize the 2-, 1-, and *-norm, leading the solution to fit the given projections, and be simultaneously sparse and low rank. Several simulations, along different data sets and optical sensing architectures, show that when the low rank property is included in the inverse problem formulation, the reconstruction quality increases up to four (dB) in terms of peak signal to noise ratio.

© 2017 Optical Society of America

Full Article  |  PDF Article
More Like This
Hyperspectral image denoising using the robust low-rank tensor recovery

Chang Li, Yong Ma, Jun Huang, Xiaoguang Mei, and Jiayi Ma
J. Opt. Soc. Am. A 32(9) 1604-1612 (2015)

Compressive spectral image reconstruction using deep prior and low-rank tensor representation

Jorge Bacca, Yesid Fonseca, and Henry Arguello
Appl. Opt. 60(14) 4197-4207 (2021)

Joint segmentation and reconstruction of hyperspectral data with compressed measurements

Qiang Zhang, Robert Plemmons, David Kittle, David Brady, and Sudhakar Prasad
Appl. Opt. 50(22) 4417-4435 (2011)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (7)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (12)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (18)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Metrics

Select as filters


Select Topics Cancel
© Copyright 2022 | Optica Publishing Group. All Rights Reserved