High Accurate and Efficient 3D Network for Image Reconstruction of Diffractive-Based Computational Spectral Imaging

Diffractive optical imaging spectroscopy as a promising miniaturized and high throughput portable spectral imaging technique suffers from the problem of low precision and slow speed, which limits its wide use in various applications. To reconstruct the diffractive spectral image more accurately and fast, a three-dimensional spectrum recovery algorithm is proposed in this paper. The algorithm takes advantage of a neural network for image reconstruction which consists of a U-Net architecture with 3D convolutional layers to improve the processing precision and speed. Numerical experiments are conducted to prove its effectiveness. It is shown that the mean peak signal-to-noise ratio (MPSNR) of the recovered image relative to the original image is improved by 1.8 dB in comparison to other traditional methods. In addition, the obtained mean structural similarity (MSSIM) of 0.91 meets the standard of discrimination to human eyes. Moreover, the algorithm runs in just 0.36 s, which is faster than other traditional methods. 3D convolutional networks play a critical role in performance improvement. Improvements in processing speed and accuracy have greatly benefited the realization and application of diffractive optical imaging spectroscopy. The new algorithm with high accuracy and fast speed has a great potential application in diffraction lens spectroscopy and paves a new way for emerging more portable spectral imaging technique.