Lightweight computational spectrometer enabled by learned high-correlation optical filters.

A neural network (NN) computational spectrometer has high reconstruction accuracy and a fast operation speed; however, this type of spectrometer also occupies a large amount of storage in an embedded system due to the excessive computation volume. Contrarily, conventional algorithms such as gradient projection for sparse reconstruction (GPSR) take up less storage, but their spectral reconstruction accuracy is much lower than that of an NN. The major reason is that the performance of a GPSR depends greatly on the non-correlation property of optical filters which may pose challenges for optical filters design and fabrication. In this study, a GPSR algorithm, known as NN-GPSR, is applied to achieve high-precision spectral reconstruction enabled by NN-learned highly correlated filters. A group of NN-learned filters shows high-correlation work as the encoder, and an optimized GPSR algorithm works as the decoder. In this case, large computation volume is exempt and prior knowledge of tens of thousands of images are exploited to get appropriate optical filters design. The experiment data indicate that the NN-GPSR performs well in the reconstructing spectrum and requires far less storage.