期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 7, 页码 4659-4669出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2892183
关键词
Denoising; Gaussian process regression (GPR); high-resolution radar; image series
类别
资金
- National Natural Science Foundation of China [61522114, 61631019]
- NSAF [U1430123]
- Foundation for the Author of National Excellent Doctoral Dissertation of P.R. China [201448]
- Young Scientist Award of Shaanxi Province [2016KJXX-82]
We address the problem of image series denoising for high-resolution radar in a nonparametric Bayesian framework. By exploiting the characteristics of amplitude variation at different pixels in the image series, we impose the Gaussian process (GP) model to the corresponding time series of each pixel and achieve effective image series denoising by GP regression. Particularly, the model parameters are solved conveniently by the maximum likelihood estimation. Compared with available denoising techniques in the data domain, spatial domain, and image frequency domain, the proposed method has exhibited more flexibility in data description and better performance in structure preserving and denoising, especially in low signal-to-noise ratio scenarios.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据