A Low-Cost Computational Spectrometer Based on a Trained Sparse Base Matrix

TL;DR

This paper introduces a low-cost computational spectrometer using a trained sparse base matrix to achieve high reconstruction accuracy.

Contribution

The novel approach uses a trained sparse base matrix to improve spectral reconstruction accuracy with low-cost materials.

Findings

Using PMMA filters with a trained sparse base matrix improves spectral reconstruction accuracy.

The spectrometer prototype achieved high-resolution spectral measurements in experiments.

Simulation results show good performance of the proposed method.

Abstract

Computational spectrometers based on coded measurement and computational reconstruction have great application prospects. This paper proposes a computational spectrometer that has a low cost, is easy to implement in hardware, and has high reconstruction accuracy. The proposed computational spectrometer uses low-cost but highly correlated polymethyl methacrylate (PMMA) material as broadband encoding filters, which could affect spectral reconstruction accuracy. To alleviate this issue, we decoupled the sensing matrix, which is the product of the measurement matrix and sparse base matrix, and subsequently optimized the sparse base matrix independently. Enlightened by the neural network method, an over-complete dictionary was trained based on the public spectral dataset, which was used as the required sparse base matrix for reconstruction. Through this method, we achieved good reconstruction results in simulation. In experiments, the spectrometer prototype can achieve a high-resolution spectral measurements, demonstrating the feasibility of a low-cost computational spectrometer based on the trained sparse base matrix.

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