Wang Jiajia, Zhang Fuyang, Zhou Xinhui, Shen Xiao, Niu Qiaoli, Yang Tao
Opt Express. 2024 Aug 12;32(17):30632-30641. doi: 10.1364/OE.527589.
Computational spectrometers are explored to overcome the disadvantages of large size, narrow bandwidth and low spectral resolution suffered by conventional spectrometers. However, expensive spectral encoders and unstable algorithms impede widespread applications of the computational spectrometers. In this paper, we propose a neural network (NN)-based miniaturized spectrometer with a frosted glass as its spectral encoder. The frosted glass has the merits of easy fabrication, low loss, and high throughput. In order to evaluate the reconstruction ability, several frequently used algorithms such as the multilayer perceptron (MLP), convolutional neural network (CNN), residual convolutional neural network (ResCNN), and Tikhonov regularization are adopted to reconstruct different types of spectra in sequence. Experimental results show that the reconstruction performance of the MLP is better than other algorithms. By using the MLP network, the average mean squared error is 1.38 × 10 and the reconstruction time is 16 µs. At the same time, a spectral resolution of 1.4 nm and a wavelength detection range of 420 nm-700 nm are realized. The effectiveness of this approach is also demonstrated by implementing a reconstruction for an unseen multi-peak spectrum. Equipped with the size, low cost, real time, broad-band, and high-resolution spectrometer, one may envision many portable wavelength analysis applications.
人们探索了计算光谱仪，以克服传统光谱仪存在的体积大、带宽窄和光谱分辨率低等缺点。然而，昂贵的光谱编码器和不稳定的算法阻碍了计算光谱仪的广泛应用。在本文中，我们提出了一种基于神经网络（NN）的小型光谱仪，其采用磨砂玻璃作为光谱编码器。磨砂玻璃具有易于制造、低损耗和高通量的优点。为了评估重建能力，依次采用了几种常用算法，如多层感知器（MLP）、卷积神经网络（CNN）、残差卷积神经网络（ResCNN）和蒂霍诺夫正则化，来重建不同类型的光谱。实验结果表明，MLP的重建性能优于其他算法。通过使用MLP网络，平均均方误差为1.38×10，重建时间为16微秒。同时，实现了1.4纳米的光谱分辨率和420纳米至700纳米的波长检测范围。通过对一个未见过的多峰光谱进行重建，也证明了该方法的有效性。配备这种尺寸小、成本低、实时、宽带和高分辨率的光谱仪，人们可以设想出许多便携式波长分析应用。
