Spectral image fusion for increasing the spatio-spectral resolution through side information

Abstract

Compressive spectral imaging (CSI) allows the acquisition of the spectral information of a three-dimensional scenes by using coded projections in a sensor with lower dimension. However, the compressed sampling of information with simultaneously high spatial and high spectral resolution demands expensive high-resolution sensors. One of the main challenges in CSI is to obtain a high-quality image of high-resolution reconstructions using low-cost architectures. Single pixel camera is an approach that has had a high impact in spectroscopy, due to its low-cost implementation compared to CSI architectures with 2D sensors. On the other hand, recent works have been shown that image fusion using measurements from a CSI sensor based on side information leads to improvement in the quality of the fused image. This work proposes a spectral image fusion methodology for increasing the spatio-spectral resolution through side information and at the same time improve the reconstruction quality of the data cube with a low-cost architecture, optimizing the similarity of the reconstructed spectral image with each sensor. Simulations and experimental results for the proposed methodology show that improve the quality of the reconstruction in up to 11 dB with respect to the traditional approach of upsampling the single pixel image reconstruction through bilinear interpolation.