  Compressive spectral imaging (CSI) has attracted significant attention since
it employs synthetic apertures to codify spatial and spectral information,
sensing only 2D projections of the 3D spectral image. However, these optical
architectures suffer from a trade-off between the spatial and spectral
resolution of the reconstructed image due to technology limitations. To
overcome this issue, compressive spectral image fusion (CSIF) employs the
projected measurements of two CSI architectures with different resolutions to
estimate a high-spatial high-spectral resolution. This work presents the fusion
of the compressive measurements of a low-spatial high-spectral resolution coded
aperture snapshot spectral imager (CASSI) architecture and a high-spatial
low-spectral resolution multispectral color filter array (MCFA) system. Unlike
previous CSIF works, this paper proposes joint optimization of the sensing
architectures and a reconstruction network in an end-to-end (E2E) manner. The
trainable optical parameters are the coded aperture (CA) in the CASSI and the
colored coded aperture in the MCFA system, employing a sigmoid activation
function and regularization function to encourage binary values on the
trainable variables for an implementation purpose. Additionally, an
unrolling-based network inspired by the alternating direction method of
multipliers (ADMM) optimization is formulated to address the reconstruction
step and the acquisition systems design jointly. Finally, a spatial-spectral
inspired loss function is employed at the end of each unrolling layer to
increase the convergence of the unrolling network. The proposed method
outperforms previous CSIF methods, and experimental results validate the method
with real measurements.
