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

Abstract

The computational spectrometer has significant potential for portable in situ applications. Encoding and reconstruction are the most critical technical procedures. In encoding, the random mass production and selection method lacks quantitative designs which leads to low encoding efficiency. In reconstruction, traditional spectrum reconstruction algorithms such as matching tracking and gradient descent demonstrate disadvantages like limited accuracy and efficiency. In this paper, we propose a new lightweight convolutional neural network called the wide-spectrum encoding and reconstruction neural network (WER-Net), which includes optical filters, quantitative spectral transmittance encoding, and fast spectral reconstruction of the encoded spectral information. The spectral transmittance curve obtained by WER-net can be fabricated through the inverse design network. The spectrometer developed based on WER-net experimentally demonstrates that it can achieve a 2-nm high resolution. In addition, the spectral transmittance encoding curve trained by WER-Net has also achieved good performance in other spectral reconstruction algorithms.