IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 卷:14 |
Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior | |
Yi Chang1  Tai-Xiang Jiang2  Yi-Si Luo3  Xi-Le Zhao3  Yu-Bang Zheng3  | |
[1] Artificial Intelligence Research Center, Peng Cheng Laboratory, Shenzhen, China; | |
[2] FinTech Innovation Center, Financial Intelligence and Financial Engineering Research Key Laboratory of Sichuan Province, School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, China; | |
[3] Research Center for Image and Vision Computing, School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China; | |
关键词: Convolutional neural networks (CNNs); denoising; hyperspectral image; spatial-spectral; unsupervised; | |
DOI : 10.1109/JSTARS.2021.3111404 | |
来源: DOAJ |
【 摘 要 】
Recently, deep learning-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as deep image prior (DIP)-based methods have received much attention because these methods do not require any training data. However, DIP-based methods suffer from the semiconvergence behavior, i.e., the iteration of DIP-based methods needs to terminate by referring to the ground-truth image at the optimal iteration point. In this article, we propose the spatial-spectral constrained deep image prior (S2DIP) for the HSI mixed noise removal. Specifically, we integrate the DIP, the spatial-spectral total variation regularization term, and the
【 授权许可】
Unknown