2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC) 2021
DOI: 10.1109/embc46164.2021.9629708
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Extended Blind End-member and Abundance Estimation with Spatial Total Variation for Hyperspectral Imaging

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Cited by 2 publications
(5 citation statements)
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“…While a definitive consensus is lacking, MS images (MSIs) typically feature more than three channels but fewer spectral bands than hyperspectral images [23]. In this approach, the data acquired through the different bands represent a spectral signature of tissue located in each pixel [21], [24], [25]. Next, the spectral signature can be analyzed to infere information, which can be associated with the pixel chemical composition [6], [21].…”
Section: Introductionmentioning
confidence: 99%
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“…While a definitive consensus is lacking, MS images (MSIs) typically feature more than three channels but fewer spectral bands than hyperspectral images [23]. In this approach, the data acquired through the different bands represent a spectral signature of tissue located in each pixel [21], [24], [25]. Next, the spectral signature can be analyzed to infere information, which can be associated with the pixel chemical composition [6], [21].…”
Section: Introductionmentioning
confidence: 99%
“…However, when dealing with MS imaging, a single pixel rarely contains the pure spectrum of a single component. Instead, it usually represents a mixture of spectral signatures from various targets, along with noise [24]. As a result, unmixing methods are utilized to discern these components.…”
Section: Introductionmentioning
confidence: 99%
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“…We employ two regularization techniques to achieve this: the total-variation (TV) regularizer and the Plug-and-Play (PnP) prior. The TV regularizer is applied to the reconstructed image to incorporate spatial and spectral information through pixel connections in the unmixing process [31,32]. On the other hand, the PnP technique utilizes state-of-the-art denoisers to tackle linear inverse problems in various hyperspectral image processing tasks [33][34][35][36][37].…”
Section: Introductionmentioning
confidence: 99%