Interpretable Deep Image Prior Method Inspired In Linear Mixture Model For Compressed Spectral Image Recovery

Abstract

This paper presents a recovery method for compressive spectral imaging (CSI) based on the training-data independent deep image prior approach, where the prior information of the image is learned through the weights and the structure of the neural network. Specifically, we propose an interpretable architecture inspired in the linear mixture model for spectral images, where the image is decomposed as the product between a basis matrix, known as endmembers, and a coefficient matrix, known as abundances. These matrices are learned as the weights and the features of the proposed network, respectively. Simulations and experiments show that the proposed recovery method outperforms the state-of-the-art CSI recovery methods, even against training-data dependent methods. Furthermore, the architecture structure inspired by the linear mixture model gives interpretability of some outputs that can be useful for subsequent high-level image processing.

RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imaging

“…The neural network is only used to learn the weight of the prior information, not the way to really obtain the prior information, and the neural network here is not deep, but only uses the volume product. Inspired by the linear mixture model (LMM) for spectral image, Gelvez T. et al [38] decomposed the image into a matrix, and uses the neural network to learn the weights and features of each matrix as the depth prior of the image for reconstruction.…”