LADMM-Net: An Unrolled Deep Network For Spectral Image Fusion From Compressive Data

Abstract

Image fusion aims at estimating a high-resolution spectral image from a low-spatial-resolution hyperspectral image and a low-spectral-resolution multispectral image. In this regard, compressive spectral imaging (CSI) has emerged as an acquisition framework that captures the relevant information of spectral images using a reduced number of measurements. Recently, various image fusion methods from CSI measurements have been proposed. However, these methods exhibit high running times and face the challenging task of choosing sparsity-inducing bases. In this paper, a deep network under the algorithm unrolling approach is proposed for fusing spectral images from compressive measurements. This architecture, dubbed LADMM-Net, casts each iteration of a linearized version of the alternating direction method of multipliers into a processing layer whose concatenation deploys a deep network. The linearized approach enables obtaining fusion estimates without resorting to costly matrix inversions. Furthermore, this approach exploits the benefits of learnable transforms to estimate the image details included in both the auxiliary variable and the Lagrange multiplier. Finally, the performance of the proposed technique is evaluated on two spectral image databases and one dataset captured at the laboratory. Extensive simulations show that the proposed method outperforms the state-of-the-art approaches that fuse spectral images from compressive measurements.