Deeply learned broadband encoding stochastic hyperspectral imaging

摘要

a Simplified schematic. Depending on where the light spectrum is encoded, the camera can work either in the active (upper) or passive (lower) modes. b Principle of DNN-based spectral reconstruction algorithm. The initial data captured by the monochrome camera is fed into the DNN and outputs the reconstructed 3D hyperspectral data cube. c, d Spectral profiles of laser beams with narrow bandwidth. In c, the DNN is trained by the “precise” dataset, whereas d is for the results from the DNN trained by “general” datasets. E Spectral profile of two peaks corresponding to 598.0 nm and 603.2 nm. The peak-to-peak distance is highlighted in black. In c–e, the ground truths and the DNN reconstructed results are represented by dashed (ground truth) and solid (reconstructed) curves, respectively. The graphs are normalized to their peak intensity