Radar Image Series Denoising of Space Targets Based on Gaussian Process Regression

Abstract

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.