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In this paper, we present a convolution neural network based method torecover the light intensity distribution from the overlapped dispersive spectrainstead of adding an extra light path to capture it directly for the firsttime. Then, we construct a single-path sub-Hadamard snapshot spectrometer basedon our previous dual-path snapshot spectrometer. In the proposed single-pathspectrometer, we use the reconstructed light intensity as the original lightintensity and recover high signal-to-noise ratio spectra successfully. Comparedwith dual-path snapshot spectrometer, the network based single-pathspectrometer has a more compact structure and maintains snapshot and highsensitivity. Abundant simulated and experimental results have demonstrated thatthe proposed method can obtain a better reconstructed signal-to-noise ratiospectrum than the dual-path sub-Hadamard spectrometer because of its higherlight throughput.

High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing