Rui Wen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Rui Wen has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Hejia Pan
Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China
1 shared publication - Lin Yang
Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China
1 shared publication - Yajuan Sheng
Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China
1 shared publication - Yanbo Gao
Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China
1 shared publication - Yuanhao Zheng
Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China
1 shared publication
Latest Publications
- Open Access
A Low-Cost Computational Spectrometer Based on a Trained Sparse Base Matrix
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.
Open AccessA Low-Cost Computational Spectrometer Based on a Trained Sparse Base Matrix
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.