Iterative algorithm computational spectrometer based on a single-hidden-layer neural network

Computational spectrometers have great application prospects in hyperspectral detection, and fast and high-precision in situ measurement is an important development trend. The computational spectrometer based on iterative algorithms has low requirements for computational resources and is easy to achieve hardware integration and in situ measurement. However, iterative algorithms are difficult to achieve high reconstruction accuracy due to the ill-posed nature of problems. Neural networks have powerful learning capabilities and can achieve high-precision spectral reconstruction. However, solely relying on neural network algorithms for reconstruction requires higher storage space and computing power from hardware devices, which makes it difficult to integrate large-scale neural network models into embedded systems. We propose using neural networks to alleviate the effect of the problem ill-posedness on the reconstruction results of iterative algorithms, so as to improve the reconstruction accuracy of the iterative algorithm computational spectrometers. First, spectral reconstruction was performed with iterative algorithms using a public spectral dataset. Then, a single-hidden-layer neural network was trained to establish a fitting relationship between the iterative algorithm spectral reconstruction results and the original spectrum. Finally, simulation and experimental results show that the proposed application of neural networks to alleviate the ill-posed problem of the iterative algorithm spectral reconstruction can effectively improve the reconstruction accuracy of iterative algorithm computational spectrometers with low computational resources. The research results may have good potential in achieving fast and high-precision in situ measurements of computational spectrometers.