Hyperspectral Unmixing Powered by Deep Image Priors and Denoising Regularization

Properly exploiting image properties is crucial for boosting the hyperspectral unmixing performance. Recent advanced image processing methods use deep architectures to learn image priors. However, these deep priors take effect in an implicit manner and it is nontrivial to characterize their properties. Introducing extra regularization terms is an explicit way of encoding image priors, and the plug-and-play technique enables to construct priors from data by denoisers. In this work, we propose a new unmixing framework to combine both the deep image priors (DIP) and plug-and-play (PnP) priors to further enhance the unmixing performance. The alter-nating direction method of multipliers (ADMM) framework is used to separate the optimization problem into two subproblems. The first one is solved using a U-net training step to obtain DIP, and a proximal denoising step is then used to solve the second subproblem to add denoiser priors. Experiment results demonstrate the effectiveness of our proposed method.