Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/108535
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior |
Author: | Zhang, L. Wei, W. Zhang, Y. Li, F. Shen, C. Shi, Q. |
Citation: | Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 2015, vol.2015 International Conference on Computer Vision, ICCV 2015, pp.3550-3558 |
Publisher: | IEEE |
Issue Date: | 2015 |
Series/Report no.: | IEEE International Conference on Computer Vision |
ISBN: | 9781467383912 |
ISSN: | 1550-5499 |
Conference Name: | IEEE International Conference on Computer Vision (11 Dec 2015 - 18 Dec 2015 : Santiago, Chile) |
Statement of Responsibility: | Lei Zhang, Wei Wei, Yanning Zhang, Fei Li, Chunhua Shen, Qinfeng Shi |
Abstract: | To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study. A matrix based hierarchical prior is first proposed to represent the spectral structured sparsity and spatial unknown manifold structure of HSI simultaneously. Then, a latent variable Bayes model is introduced to learn the sparsity prior and estimate the noise jointly from measurements. The learned prior can fully represent the inherent 3D structure of HSI and regulate its shape based on the estimated noise level. Thus, with this learned prior, the proposed method improves the reconstruction accuracy significantly and shows strong robustness to unknown noise in HCS. Experiments on four real hyperspectral datasets show that the proposed method outperforms several state-of-the-art methods on the reconstruction accuracy of HSI. |
Rights: | © 2015 IEEE |
DOI: | 10.1109/ICCV.2015.405 |
Published version: | http://dx.doi.org/10.1109/iccv.2015.405 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
RA_hdl_108535.pdf Restricted Access | Restricted Access | 555.66 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.