Deep-learning-based hyperspectral imaging through a RGB camera

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.