AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising

Spectral unmixing has been extensively studied with a variety of methods and is used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to automatically learn the structure information. In particular, autoencoder (AE)-based architectures are elaborately designed to solve blind unmixing and model complex nonlinear mixtures. Nevertheless, these methods perform unmixing task as black boxes and lack interpretability. On the other hand, conventional unmixing methods carefully design the regularizer to add explicit information, in which algorithms such as plug-and-play (PnP) strategies utilize off-the-shelf denoisers to plug powerful priors. In this article, we propose a generic unmixing framework to integrate the AE network with regularization by denoising (RED), named AE-RED. More specifically, we decompose the unmixing optimized problem into two subproblems. The first one is solved using deep AEs to implicitly regularize the estimates and model the mixture mechanism. The second one leverages the denoiser to bring in explicit information. In this way, both the characteristics of the deep AE-based unmixing methods and priors provided by denoisers are merged into our well-designed framework to enhance the unmixing performance. The experiment results on both synthetic and real datasets show the superiority of our proposed framework compared with state-of-the-art unmixing approaches.