@inproceedings{08a01ec804a54abdb32d4dad46b7f957,
title = "Hyperspectral Unmixing Via Plug-And-Play Priors",
abstract = "Hyperspectral unmixing aims at separating a mixed pixel into a set of pure spectral signatures and their corresponding fractional abundances. Investigating prior spatial and spectral information to regularize the unmixing problem can effectively improve the estimation performance. However, handcrafting a powerful regularizer is a non-trivial task and complex regularizers introduce extra difficulties in solving the optimization problem. In this paper, we present a flexible spectral unmixing method using plug-and-play priors. This method benefits from the alternating direction method of multipliers (ADMM) to decompose the optimization problem into iterative subproblems and incorporates the image denoisers as prior models in a subproblem. In this form, we can plug in various image denoising operations to bypass handcrafting regularizers. We demonstrate the superiority of the proposed unmixing method comparing with other state-of-the-art methods both on synthetic data and real airborne data.",
keywords = "ADMM, Hyperspectral unmixing, image denoising, plug-and-play, prior modeling",
author = "Xiuheng Wang and Min Zhao and Jie Chen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference date: 25-09-2020 Through 28-09-2020",
year = "2020",
month = oct,
doi = "10.1109/ICIP40778.2020.9190817",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1063--1067",
booktitle = "2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings",
}