Fast Frequency-Domain Compressed Sensing Analysis for High-Density Super-Resolution Imaging Using Orthogonal Matching Pursuit
Single-molecule localization methods play a vital role in a localization-based super-resolution fluorescence microscopy. However, it is difficult for conventional localization schemes based on the Gaussian fitting to locate overlapped high-density fluorescent emitters. Currently, in the spatial domain, the compressive-sensing-based algorithm (CSSTORM) can localize high-emitter-density images. However, the computational cost of this approach is extremely high, which limits its practical application. Here, we propose an alternative frequency-domain compressed sensing (FD-CS) technique for fast super-resolution imaging. Unlike the CSSTORM method, which is a measurement matrix based on the point spread function, a Fourier dictionary designed in the frequency domain and orthogonal matching pursuit is used to reliably recover the original signal. The simulation and experimental results prove that the FD-CS is 1000 times faster than CSSTORM with CVX and ten times faster than that with L1-Homotopy with almost the same localization accuracy and recall rate. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to a super-resolution image analysis..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
IEEE Photonics Journal - 11(2019), 1, Seite 8 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Saiwen Zhang [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
Applied optics. Photonics |
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| doi: |
10.1109/JPHOT.2018.2884730 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ056363362 |
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| 520 | |a Single-molecule localization methods play a vital role in a localization-based super-resolution fluorescence microscopy. However, it is difficult for conventional localization schemes based on the Gaussian fitting to locate overlapped high-density fluorescent emitters. Currently, in the spatial domain, the compressive-sensing-based algorithm (CSSTORM) can localize high-emitter-density images. However, the computational cost of this approach is extremely high, which limits its practical application. Here, we propose an alternative frequency-domain compressed sensing (FD-CS) technique for fast super-resolution imaging. Unlike the CSSTORM method, which is a measurement matrix based on the point spread function, a Fourier dictionary designed in the frequency domain and orthogonal matching pursuit is used to reliably recover the original signal. The simulation and experimental results prove that the FD-CS is 1000 times faster than CSSTORM with CVX and ten times faster than that with L1-Homotopy with almost the same localization accuracy and recall rate. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to a super-resolution image analysis. | ||
| 650 | 4 | |a Fluorescence microscopy | |
| 650 | 4 | |a image reconstruction techniques | |
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| 650 | 4 | |a super-resolution | |
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| 653 | 0 | |a Optics. Light | |
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| 700 | 0 | |a Danni Chen |e verfasserin |4 aut | |
| 700 | 0 | |a Siwei Li |e verfasserin |4 aut | |
| 700 | 0 | |a Bin Yu |e verfasserin |4 aut | |
| 700 | 0 | |a Junle Qu |e verfasserin |4 aut | |
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