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WER-Net: A New Lightweight Wide-Spectrum Encoding and Reconstruction Neural Network Applied to Computational

Xinran Ding¹, Lin Yang^1,2, Mingyang Yi¹

¹School of Information Science and Engineering, Shandong University, Qingdao 266237, China.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

We developed a new neural network, the wide-spectrum encoding and reconstruction neural network (WER-Net), to improve computational spectrometers. WER-Net enhances spectral encoding and reconstruction for more accurate and efficient portable spectroscopy.

Keywords:

computational spectrometer convolutional neural network hierarchical optimization wide-spectrum encoding

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Area of Science:

Spectroscopy
Optical Engineering
Machine Learning

Background:

Computational spectrometers offer potential for portable in situ analysis.
Current encoding methods lack quantitative design, reducing efficiency.
Existing reconstruction algorithms have limited accuracy and speed.

Purpose of the Study:

To introduce a novel lightweight convolutional neural network, WER-Net, for improved spectral encoding and reconstruction.
To address the limitations of current computational spectrometer technologies.
To enable high-resolution portable spectroscopy.

Main Methods:

Developed a wide-spectrum encoding and reconstruction neural network (WER-Net).
Incorporated optical filters and quantitative spectral transmittance encoding.
Utilized an inverse design network for fabricating spectral transmittance curves.

Main Results:

The WER-Net based spectrometer achieved a 2-nm high spectral resolution experimentally.
The proposed quantitative encoding method significantly improved efficiency.
WER-Net demonstrated good performance in other spectral reconstruction algorithms.

Conclusions:

WER-Net offers a significant advancement for computational spectrometers.
The developed method enables high-resolution, portable, and efficient spectral analysis.
The quantitative encoding strategy shows broad applicability in spectral reconstruction.