  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 hyperspectral images 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 were 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.
