2020
DOI: 10.1016/j.optlaseng.2019.105973
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Hyperspectral phase imaging based on denoising in complex-valued eigensubspace

Abstract: A new denoising algorithm for hyperspectral complex domain data has been developed and studied. This algorithm is based on the complex domain block-matching 3D filter including the 3D Wiener arXiv:1907.03104v1 [eess.IV] 6 Jul 2019 A PREPRINT -JULY 9, 2019 filtering stage. The developed algorithm is applied and tuned to work in the singular value decomposition (SVD) eigenspace of reduced dimension. The accuracy and quantitative advantage of the new algorithm are demonstrated in simulation tests and in processin… Show more

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Cited by 24 publications
(17 citation statements)
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“…A sliding window version of CCF was developed for objects with discontinuous and fast varying spectral characteristics. 27 according to Eq. ( 14) obtained from an uniform initial guess for hðx; yÞ.…”
Section: Regularization By Sparsity-based Filtersmentioning
confidence: 99%
“…A sliding window version of CCF was developed for objects with discontinuous and fast varying spectral characteristics. 27 according to Eq. ( 14) obtained from an uniform initial guess for hðx; yÞ.…”
Section: Regularization By Sparsity-based Filtersmentioning
confidence: 99%
“…Here we briefly describe Complex Cube Filter (CCF) algorithm (for more detailed description please see the paper [10]). The algorithm is used with the following notations:U^Λ¯(x,y)=scriptCCF{normalZtrueΛ¯(x,y),Λ¯Λ}. Here, Λ¯ is a set of slices to be denoised.…”
Section: Problem Formulationmentioning
confidence: 99%
“…Traditionally, many rooting techniques are used for denoising in HSDH, but they do not help much in noise suppression, working for low noise levels [7,9], but failing in the high ones [10]. It is explained by a slice-wise separate filtering which does not process all HS cube slices jointly.…”
Section: Introductionmentioning
confidence: 99%
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