Low cost hyperspectral imaging using deep learning based spectral reconstruction

The increasing number of applications of hyperspectral imaging results in a high demand for low cost, mobile devices. We propose a multispectral imaging (MSI) system based on time-multiplexed lighting using RGB Light Emitting Diodes (LED). We train a deep neural network that maps low dimensional multispectral input onto high dimensional hyperspectral (HSI) output that is collected with a HSI camera covering the range of 400 – 950 nm. Results on the 24 colour patches of the Macbeth colour checker chart show that with only five multispectral bands, a very accurate reconstruction of HSI data can be achieved.