A low-cost near-infrared spectrometer based on fiber beam scanning and linear variable filter (LVF) was proposed and demonstrated. The key elements of the spectrometer include a piezoelectric driven fiber scanner, an LVF monochromator, and a single point detector. The single fiber scanner provides a new way to continuously sweep the LVF. A PIN photodiode behind the LVF monochromator can acquire more spectral information than the traditional linear array detection. The performance of the spectrometer was verified with a narrow band tunable laser and a broadband light source. With the help of a deconvolution algorithm, the spectral resolution of the proposed spectrometer is up to 0.32% of the center wavelength (≈5 nm@1550 nm).
Hyperspectral image (HSI) contains both spatial pattern and spectral information, which has been widely used in food safety, remote sensing, and medical detection. However, the acquisition of HSIs is usually costly due to the complicated apparatus for the acquisition of optical spectrum. Recently, it has been reported that HSI can be reconstructed from single RGB image using convolution neural network (CNN) algorithms. Compared with the traditional hyperspectral cameras, the method based on CNN algorithms is simple, portable, and low cost. In this study, we focused on the influence of the RGB camera spectral sensitivity (CSS) on the HSI. A xenon lamp incorporated with a monochromator was used as the standard light source to calibrate the CSS. And the experimental results show that the CSS plays a significant role in the reconstruction accuracy of an HSI. In addition, we proposed a new HSI reconstruction network where the dimensional structure of the original hyperspectral datacube was modified by 3D matrix transpose to improve the reconstruction accuracy.
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