21 April 2016 Removing sparse noise from hyperspectral images with sparse and low-rank penalties
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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.
© 2016 SPIE and IS&T
Snigdha Tariyal, Snigdha Tariyal, Hemant K. Aggarwal, Hemant K. Aggarwal, Angshul Majumdar, Angshul Majumdar, } "Removing sparse noise from hyperspectral images with sparse and low-rank penalties," Journal of Electronic Imaging 25(2), 020501 (21 April 2016). https://doi.org/10.1117/1.JEI.25.2.020501 . Submission:

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