Development of a fast calibration method for image mapping spectrometry

Qi Cui; Jongchan Park; Rishyashring R. Iyer; Rishyashring R. Iyer; Mantas Žurauskas; Stephen A. Boppart; Stephen A. Boppart; R. Theodore Smith; Liang Gao

doi:10.1364/AO.395988

Applied Optics
Vol. 59,
Issue 20,
pp. 6062-6069
(2020)
•https://doi.org/10.1364/AO.395988

Development of a fast calibration method for image mapping spectrometry

Qi Cui, Jongchan Park, Rishyashring R. Iyer, Mantas Žurauskas, Stephen A. Boppart, R. Theodore Smith, and Liang Gao

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Qi Cui, Jongchan Park, Rishyashring R. Iyer, Mantas Žurauskas, Stephen A. Boppart, R. Theodore Smith, and Liang Gao, "Development of a fast calibration method for image mapping spectrometry," Appl. Opt. 59, 6062-6069 (2020)

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Table of Contents Category
- Imaging Systems, Image Processing, and Displays
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About this Article
History
- Original Manuscript: April 23, 2020
- Revised Manuscript: June 6, 2020
- Manuscript Accepted: June 8, 2020
- Published: July 7, 2020

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Abstract

An image mapping spectrometer (IMS) is a snapshot hyperspectral imager that simultaneously captures both the spatial ($x$, $y$) and spectral ($\lambda$) information of incoming light. The IMS maps a three-dimensional (3D) datacube ($x$, $y$, $\lambda$) to a two-dimensional (2D) detector array ($x$, $y$) for parallel measurement. To reconstruct the original 3D datacube, one must construct a lookup table that connects voxels in the datacube and pixels in the raw image. Previous calibration methods suffer from either low speed or poor image quality. We herein present a slit-scan calibration method that can significantly reduce the calibration time while maintaining high accuracy. Moreover, we quantitatively analyzed the major artifact in the IMS, the striped image, and developed three numerical methods to correct for it.

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Data Availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.

Code Availability

Codes used for this work are available from the corresponding author upon reasonable request.

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Wavelength	532 nm
Number of microlenses	40
Image slice per microlens	12
NA of microlens	0.125
Pixel pitch of the camera	7.4 µm
Reflectivity of mirror facets	Uniform distribution from 95% to 100%
Defocus of microlenses	Gaussian distribution, $m e a n = 0$ , standard $d e v i a t i o n = σ$
Method Name	Gaussian Noise Standard Deviation	Defocus $σ$ (in mm)	Striping Factor $c$ (before)	Striping Factor $c$ (after)	Striping Factor Change (in Percent)
Fourier domain filtering	0	0.05	0.0158	0.0148	−6.33%
	0	0.1	0.022	0.0188	−14.55%
	0	0.2	0.0615	0.0433	−29.59%
	0.01	0.1	0.0244	0.0215	−11.89%
	0.1	0.1	0.1062	0.1056	−0.57%
Deconvolution	0	0.05	0.0158	0	−100%
	0	0.1	0.0221	0	−100%
	0	0.2	0.0615	0.0012	−98.05%
	0.01	0.1	0.0244	0.0105	−56.97%
	0.1	0.1	0.1062	0.1081	$+ 1.79$ %
Datacube normalization	0	0.05	0.0158	0.004	−74.68%
	0	0.1	0.0221	0.0155	−29.86%
	0	0.2	0.0615	0.0562	−8.62%
	0.01	0.1	0.0244	0.0213	−12.7%
	0.1	0.1	0.1062	0.1503	$+ 41.53$ %

Microlens Number	40
Defocus of microlenses	Gaussian distribution, $m e a n = 0$ , $s t a n d a r d d e v i a t i o n = σ$
$σ$ range	From 0 to 0.1 mm, step is 0.0005 mm
Simulated microlens groups	For each $σ$ , 1000 groups (40 microlenses per group)

Wavelength	532 nm
Number of microlenses	40
Image slice per microlens	12
NA of microlens	0.125
Pixel pitch of the camera	7.4 µm
Reflectivity of mirror facets	Uniform distribution from 95% to 100%
Defocus of microlenses	Gaussian distribution, $m e a n = 0$ , standard $d e v i a t i o n = σ$
Method Name	Gaussian Noise Standard Deviation	Defocus $σ$ (in mm)	Striping Factor $c$ (before)	Striping Factor $c$ (after)	Striping Factor Change (in Percent)
Fourier domain filtering	0	0.05	0.0158	0.0148	−6.33%
	0	0.1	0.022	0.0188	−14.55%
	0	0.2	0.0615	0.0433	−29.59%
	0.01	0.1	0.0244	0.0215	−11.89%
	0.1	0.1	0.1062	0.1056	−0.57%
Deconvolution	0	0.05	0.0158	0	−100%
	0	0.1	0.0221	0	−100%
	0	0.2	0.0615	0.0012	−98.05%
	0.01	0.1	0.0244	0.0105	−56.97%
	0.1	0.1	0.1062	0.1081	$+ 1.79$ %
Datacube normalization	0	0.05	0.0158	0.004	−74.68%
	0	0.1	0.0221	0.0155	−29.86%
	0	0.2	0.0615	0.0562	−8.62%
	0.01	0.1	0.0244	0.0213	−12.7%
	0.1	0.1	0.1062	0.1503	$+ 41.53$ %

Microlens Number	40
Defocus of microlenses	Gaussian distribution, $m e a n = 0$ , $s t a n d a r d d e v i a t i o n = σ$
$σ$ range	From 0 to 0.1 mm, step is 0.0005 mm
Simulated microlens groups	For each $σ$ , 1000 groups (40 microlenses per group)

Abstract

Data Availability

Cited By

Figures (10)

Tables (2)

Equations (6)

Applied Optics