Compact colorful compressive spectral imager based on deep learning reconstruction

Leveraging the spatio-spectral modulation and sophisticated reconstruction algorithms, the colorful compressive spectral imaging (CCSI) method can reconstruct a three-dimensional spectral image from a single compressive measurement. Primary CCSI systems enhance the modulation freedom through the combination of colorful coding mask (CCM) and dispersive element, but this kind of system has complex structure that limits the miniaturization of system. Furthermore, the reconstruction quality of CCSI systems can be further improved by using deep learning algorithms. This paper proposes a compact CCSI method based on deep learning reconstruction, which tries to reduce the volume of system by attaching the CCM to the detector. The combination of CCM and RGB detector enhances the modulation freedom. Additionally, a Transformer-based deep learning algorithm is used to obtain promising reconstruction results of the target spectral images. Results of both simulations and experiments demonstrate the effectiveness of the proposed compact CSSI method.