Deep learning super resolution for high-speed excitation emission matrix measurements

Umberto Michelucci; Silvan Fluri; Michael Baumgartner; Francesca Venturini

doi:10.1117/12.2647589

15 March 2023 Deep learning super resolution for high-speed excitation emission matrix measurements

Umberto Michelucci, Silvan Fluri, Michael Baumgartner, Francesca Venturini

Proceedings Volume 12438, AI and Optical Data Sciences IV; 124380I (2023) https://doi.org/10.1117/12.2647589
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

In many optical experiments, a long measurement time is necessary to collect enough information and improve the signal-to-noise ratio. This happens, for example, in total luminescence spectroscopy (TLS) where the data is acquired as excitation-emission matrices (EEMs). An EEM is an unique chemical fingerprint of the analyzed substance that allows its comprehensive characterization. To collect a high-resolution EEM, it is necessary to scan both the excitation and the emission wavelengths in small steps and, for each step, to collect the light for a long time to maximize the signal-to-noise ratio. Therefore, acquiring a high-resolution excitation emission matrix can take more than an hour, depending on the size of the wavelength steps, the intensity of the signal, and the spectral range to be analyzed. This paper proposes a new method to reconstruct a high-resolution EEM from low-resolution one using deep learning super-resolution techniques. Specifically, this work proposes a new artificial neural network architecture, a sub-pixel convolutional neural network, designed to be applied to fluorescence EEM images. The code used is made available via a GitHub repository with instructions for applying transfer learning to different types of images.

Conference Presentation

Citation Download Citation

Umberto Michelucci, Silvan Fluri, Michael Baumgartner, and Francesca Venturini "Deep learning super resolution for high-speed excitation emission matrix measurements", Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380I (15 March 2023); https://doi.org/10.1117/12.2647589

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