JEI Letters

Removing sparse noise from hyperspectral images with sparse and low-rank penalties

[+] Author Affiliations
Snigdha Tariyal, Hemant Kumar Aggarwal, Angshul Majumdar

Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India

J. Electron. Imaging. 25(2), 020501 (Apr 21, 2016). doi:10.1117/1.JEI.25.2.020501
History: Received July 25, 2015; Accepted March 30, 2016
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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.

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Topics

Denoising ; Matrices

Citation

Snigdha Tariyal ; Hemant Kumar Aggarwal and Angshul Majumdar
"Removing sparse noise from hyperspectral images with sparse and low-rank penalties", J. Electron. Imaging. 25(2), 020501 (Apr 21, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.2.020501


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