TY - JOUR
T1 - Opti-MSFA
T2 - A toolbox for generalized design and optimization of multispectral filter arrays
AU - Sawyer, Travis W.
AU - Taylor-Williams, Michaela
AU - Tao, Ran
AU - Xia, Ruqiao
AU - Williams, Calum
AU - Bohndiek, Sarah E.
N1 - Funding Information:
Acknowledgements. T.W.S acknowledges the financial support of the Winton Foundation. M. T-W. acknowledges the financial support of the Sir General John Monash Foundation and the Cambridge Trust. R.T. and R.X. acknowledge the financial support of the EPSRC Centre for Doctoral Training in Connected Electronic and Photonic Systems (EP/S022139/1). C.W. acknowledges the financial support of the Wellcome Trust (Interdisciplinary Fellowship) and Wolfson College, Cambridge. S.E.B. acknowledges support from the EPSRC (EP/R003599/1) and CRUK (C9545/A29580)
Funding Information:
Funding. Cancer Research UK (C9545/A29580); Engineering and Physical Sciences Research Council (EP/R003599/1); Wolfson College, University of Cambridge; Wellcome Trust; EPSRC Centre for Doctoral Training in Connected Electronic and Photonic Systems (EP/S022139/1); Cambridge Trust; Sir General John Monash Foundation; Winton Foundation.
Publisher Copyright:
© 2022 OSA - The Optical Society. All rights reserved.
PY - 2022/2/28
Y1 - 2022/2/28
N2 - Multispectral imaging captures spatial information across a set of discrete spectral channels and is widely utilized across diverse applications such as remote sensing, industrial inspection, and biomedical imaging. Multispectral filter arrays (MSFAs) are filter mosaics integrated atop image sensors that facilitate cost-effective, compact, snapshot multispectral imaging. MSFAs are pre-configured based on application-where filter channels are selected corresponding to targeted absorption spectra-making the design of optimal MSFAs vital for a given application. Despite the availability of many design and optimization approaches for spectral channel selection and spatial arrangement, major limitations remain. There are few robust approaches for joint spectral-spatial optimization, techniques are typically only applicable to limited datasets and most critically, are not available for general use and improvement by the wider community. Here, we reconcile current MSFA design techniques and present Opti-MSFA: A Python-based open-access toolbox for the centralized design and optimization of MSFAs. Opti-MSFA incorporates established spectral-spatial optimization algorithms, such as gradient descent and simulated annealing, multispectral-RGB image reconstruction, and is applicable to user-defined input of spatial-spectral datasets or imagery. We demonstrate the utility of the toolbox by comparing against other published MSFAs using the standard hyperspectral datasets Samson and Jasper Ridge, and further show application on experimentally acquired fluorescence imaging data. In conjunction with end-user input and collaboration, we foresee the continued development of Opti-MSFA for the benefit of the wider research community.
AB - Multispectral imaging captures spatial information across a set of discrete spectral channels and is widely utilized across diverse applications such as remote sensing, industrial inspection, and biomedical imaging. Multispectral filter arrays (MSFAs) are filter mosaics integrated atop image sensors that facilitate cost-effective, compact, snapshot multispectral imaging. MSFAs are pre-configured based on application-where filter channels are selected corresponding to targeted absorption spectra-making the design of optimal MSFAs vital for a given application. Despite the availability of many design and optimization approaches for spectral channel selection and spatial arrangement, major limitations remain. There are few robust approaches for joint spectral-spatial optimization, techniques are typically only applicable to limited datasets and most critically, are not available for general use and improvement by the wider community. Here, we reconcile current MSFA design techniques and present Opti-MSFA: A Python-based open-access toolbox for the centralized design and optimization of MSFAs. Opti-MSFA incorporates established spectral-spatial optimization algorithms, such as gradient descent and simulated annealing, multispectral-RGB image reconstruction, and is applicable to user-defined input of spatial-spectral datasets or imagery. We demonstrate the utility of the toolbox by comparing against other published MSFAs using the standard hyperspectral datasets Samson and Jasper Ridge, and further show application on experimentally acquired fluorescence imaging data. In conjunction with end-user input and collaboration, we foresee the continued development of Opti-MSFA for the benefit of the wider research community.
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U2 - 10.1364/OE.446767
DO - 10.1364/OE.446767
M3 - Article
C2 - 35299518
AN - SCOPUS:85125166509
VL - 30
SP - 7591
EP - 7611
JO - Optics Express
JF - Optics Express
SN - 1094-4087
IS - 5
ER -