Deep Low-Dimensional Spectral Image Representation for Compressive Spectral Reconstruction

Abstract

This work introduces a deep low-dimensional spectral image representation designed for compressive spectral reconstruction. By learning compact latent spaces tailored to the underlying physics of snapshot compressive spectral sensing, the framework provides structured regularization that outperforms conventional sparsity priors.