A Plug-and-Play Priors Framework for Hyperspectral Unmixing

TL;DR

This paper introduces a flexible plug-and-play priors framework for hyperspectral unmixing that leverages denoisers within an ADMM optimization scheme, improving performance over existing methods.

Contribution

It proposes a novel PnP framework that replaces handcrafted regularizers with denoisers, simplifying the unmixing process and enhancing results.

Findings

Outperforms state-of-the-art unmixing methods on synthetic data

Effective on real airborne hyperspectral data

Flexible framework adaptable to various denoisers

Abstract

Spectral unmixing is a widely used technique in hyperspectral image processing and analysis. It aims to separate mixed pixels into the component materials and their corresponding abundances. Early solutions to spectral unmixing are performed independently on each pixel. Nowadays, investigating proper priors into the unmixing problem has been popular as it can significantly enhance the unmixing performance. However, it is non-trivial to handcraft a powerful regularizer, and complex regularizers may introduce extra difficulties in solving optimization problems in which they are involved. To address this issue, we present a plug-and-play (PnP) priors framework for hyperspectral unmixing. More specifically, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into two iterative subproblems. One is a regular optimization problem depending on the forward model, and the other is a proximity operator related to the prior model and can be regarded as an image denoising problem. Our framework is flexible and extendable which allows a wide range of denoisers to replace prior models and avoids handcrafting regularizers. Experiments conducted on both synthetic data and real airborne data illustrate the superiority of the proposed strategy compared with other state-of-the-art hyperspectral unmixing methods.