Abstract
Previous research has shown that the effectiveness of selecting filter sets from among a large set of commercial broadband filters by a vector analysis method based on maximum linear independence (MLI). However, the traditional MLI approach is suboptimal due to the need to predefine the first filter of the selected filter set to be the maximum ℓ2 norm among all available filters. An exhaustive imaging simulation with every single filter serving as the first filter is conducted to investigate the features of the most competent filter set. From the simulation, the characteristics of the most competent filter set are discovered. Besides minimization of the condition number, the geometric features of the best-performed filter set comprise a distinct transmittance peak along the wavelength axis of the first filter, a generally uniform distribution for the peaks of the filters and substantial overlaps of the transmittance curves of the adjacent filters. Therefore, the best-performed filter sets can be recognized intuitively by simple vector analysis and just a few experimental verifications. A practical two-step framework for selecting optimal filter set is recommended, which guarantees a significant enhancement of the performance of the systems. This work should be useful for optimizing the spectral sensitivity of broadband multispectral imaging sensors.
Highlights
Multispectral imaging refers to imaging with more than three to several tens of narrowband or broadband spectral channels
This work should be useful for optimizing the spectral sensitivity of broadband multispectral imaging sensors
We propose a framework of selecting a broadband filter set from a large number of commercial filters according to maximum linear independence (MLI) by imaging simulation
Summary
Multispectral imaging refers to imaging with more than three to several tens of narrowband or broadband spectral channels. Broadband multispectral imaging takes advantage over narrow multispectral imaging in reconstructing smooth spectra of imaging scenes using a far fewer number of spectral measurements [7], it can significantly reduce the complexity of the needed hardware. With an equal number of spectral channels, because the broadband techniques preserve more spectral features such as transmission or absorption peaks than narrow multispectral imaging [7,8], broadband multispectral imaging should be more promising to reconstruct spectral information of imaging scenes. A broadband multispectral camera cannot acquire the spectral samples at each wavelength directly at the data acquisition stage as narrowband ones; it takes spectral images at the expense of extensive post-processing under the condition that the spectral sensitivity of the system is optimized. Optimizing the spectral sensitivity is crucial for setting up a broadband multispectral camera
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