Spectral reconstruction using neural networks in filter-array-based chip-size spectrometers

J. Wissing, Lidia Fargueta, Stephan Scheele
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Abstract

Spectral reconstruction in filter-based miniature spectrometers remains challenging due to the ill-posed nature of identifying stable solutions. Even minor deviations in sensor data can cause misleading reconstruction outcomes, particularly in the absence of proper regularization techniques. While previous research has attempted to mitigate this instability by incorporating neural networks into the reconstruction pipeline to denoise the data before reconstruction or correct it after reconstruction, these approaches have not fully resolved the underlying issue. This work functions as a proof-of-concept for data-driven reconstruction that relies exclusively on neural networks, thereby circumventing the need to address the ill-posed inverse problem. We curate a dataset holding transmission spectra from various colored foils, commonly used in theatrical, and train five distinct neural networks optimized for spectral reconstruction. Subsequently, we benchmark these networks against each other and compare their reconstruction capabilities with a linear reconstruction model to show the applicability of cognitive sensors to the problem of spectral reconstruction. In our testing, we discovered that (i) spectral reconstruction can be achieved using neural networks with an end-to-end approach, and (ii) while a classic linear model can perform equal to neural networks under optimal conditions, the latter can be considered more robust against data deviations.
在基于滤波器阵列的芯片级光谱仪中使用神经网络重建光谱
在基于滤波器的微型光谱仪中进行光谱重构仍然具有挑战性,这是因为要确定稳定的解决方案存在困难。即使是传感器数据中的微小偏差也会导致误导性的重建结果,尤其是在缺乏适当正则化技术的情况下。虽然以前的研究试图通过将神经网络纳入重建管道来缓解这种不稳定性,在重建前对数据进行去噪,或在重建后对数据进行校正,但这些方法并没有完全解决根本问题。这项工作是完全依靠神经网络进行数据驱动重建的概念验证,从而避免了解决反问题的需要。我们策划了一个数据集,其中包含戏剧中常用的各种彩色箔片的透射光谱,并训练了五个针对光谱重建进行优化的不同神经网络。随后,我们对这些网络进行了基准测试,并将它们的重构能力与线性重构模型进行了比较,以展示认知传感器在光谱重构问题上的适用性。在测试中,我们发现:(i) 使用端到端方法的神经网络可以实现光谱重建;(ii) 虽然经典线性模型在最佳条件下的性能与神经网络相当,但后者可以被认为对数据偏差具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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