Acousto-optical filters are devices in which light dispersion occurs through a crystalline translucent material. Particularly, light interacts with a sound-induced spatially distributed. Post-interaction, diffracted light can be analyzed for different purposes. Although acousto-optics has been studied for decades, practical devices applying its principles are relatively recent. Here, experimental and technical procedures are used to obtain the transfer function of an acousto-optical tunable filter (AOTF) based system used as a hyper-spectral photometer. The reflectance responses at given wavelengths are measured and adjusted from a commercially available color pattern set, while typically, those values are set up manually. We propose a semiautomatic strategy to calibrate as a single black box all components of the system including: the light source, the signal generator power with its frequency-amplitude deviation from the full radio frequency set point, the radio-frequency amplifier, the transmission lines, the piezoelectric impedance, and the filter's own transfer function among others. To achieve that, we explored the capability of neural networks with deep learning. The system's input is reflectance data measured with a spectrophotometer at wavelengths from 400 to 700 nm with a step of 10 nm. Then, the AOTF system was used to gather reflectance data from those color pattern tiles from 400 to 700 nm with a step of 1 nm. Both reflectance datasets were adjusted using the proposed deep learning neural network. Results show that it is possible to calibrate an AOTF system by using ceramic tile color patterns and measuring reference reflectance values with a spectrophotometer in the visible range. Furthermore, a neural network can be trained to learn the compensation values, deriving trustable spectral information with a better wavelength resolution.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.