An interpretable and flexible fusion prior to boost hyperspectral imaging reconstruction

Abstract

Hyperspectral image (HSI) reconstruction from the compressed measurement captured by the coded aperture snapshot spectral imager system remains a hot topic. Recently, deep-learning-based methods for HSI reconstruction have become the mainstream due to their high performance and efficiency in the testing inference. However, these learning methods do not fully utilize the abundant spectral information with proper physical spectral priors, resulting in complex architectures and unsatisfactory reconstruction performance. In this paper, we claim that the spectral low-rank property can still help these learning methods and propose a hyperspectral fusion theory, which demonstrates that full HSIs are mathematically equivalent to the closed-form combination of subspace images, mask, and measurements. Based on the above fusion theory, we propose the subspace distillation prior (SP) to efficiently cooperate with existing learning models to enhance the exploration of the spectral low-rank property. In detail, the SP can directly improve the testing inference of existing models (SP1, Section 4.1). Furthermore, SP can also be cooperated with exiting networks to formulate a new framework, which regularizes the existing models to learn the subspace images, and help to reconstruct the full HSIs from subspace images, mask, and measurements (SP2, Section 4.2). We choose six existing representative models for the HSI reconstruction experiments and find that SP1 and SP2 can, respectively, achieve improvements of 0.08 dB∼0.76 dB and 0.36 dB∼1.76 dB on the simulated datasets, demonstrating the advantage of the proposed hyperspectral fusion theory. The source code is available at https://github.com/prowDIY.