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Journal of Electronic Imaging

Removing sparse noise from hyperspectral images with sparse and low-rank penalties
Author(s): Snigdha Tariyal; Hemant K. Aggarwal; Angshul Majumdar
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Paper Abstract

In diffraction grating, at times, there are defective pixels on the focal plane array; this results in horizontal lines of corrupted pixels in some channels. Since only a few such pixels exist, the corruption/noise is sparse. Studies on sparse noise removal from hyperspectral noise are parsimonious. To remove such sparse noise, a prior work exploited the interband spectral correlation along with intraband spatial redundancy to yield a sparse representation in transform domains. We improve upon the prior technique. The intraband spatial redundancy is modeled as a sparse set of transform coefficients and the interband spectral correlation is modeled as a rank deficient matrix. The resulting optimization problem is solved using the split Bregman technique. Comparative experimental results show that our proposed approach is better than the previous one.

Paper Details

Date Published: 21 April 2016
PDF: 4 pages
J. Electron. Imag. 25(2) 020501 doi: 10.1117/1.JEI.25.2.020501
Published in: Journal of Electronic Imaging Volume 25, Issue 2
Show Author Affiliations
Snigdha Tariyal, Indraprastha Institute of Information Technology (India)
Hemant K. Aggarwal, Indraprastha Institute of Information Technology (India)
Angshul Majumdar, Indraprastha Institute of Information Technology (India)

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