射电日像仪的压缩感知和脏图高斯去噪

Compressed sensing of radio heliograph and Gaussian denoising of dirty image

摘要:

传统的综合孔径射电日像仪成像原理是根据香农采样定理,使用香农采样得到完整的频谱数据,进行反傅里叶变换得到图像.因成像设备及外界环境因素,频谱数据中伴随有大量非真实信号数据,导致所成图像产生大量噪声,通常称为脏图.射电天文领域通常采用相关Clean算法处理脏图,得到“干净”的射电图像. 为了降低了射电信号的采样成本,而且能得到更“干净”的射电图像，基于射电干涉稀疏成像与压缩感知理论,实现了从不完整的频谱中重建脏图,之后进行噪声去除:采用正交跟踪匹配与特征标志算法完成从稀疏频谱中重建脏图,之后使用三维块匹配算法去除噪声.

Abstract:

The traditional principle of synthetic aperture radio imaging is based on Shannon sampling theorem, which obtain the complete spectrum data with Shannon sampling and the inverse Fourier transform is used to generate the image. Due to the imaging equipment and external environmental factors, the spectrum data is accompanied by a large number of unreal signal, which causes a large amount of noise to be generated in the image, usually called a dirty image. In the field of radio astronomy, the related clean algorithm is usually used to process the dirty image to obtain a "clean" image. In order to reduce the sampling cost of the radio signal and obtain more "clean" radio image, based on radio interference sparse imaging and compressed sensing theory, we realize the reconstruction of the dirty image from the incomplete spectrum, and then removes the noise, orthogonal matching pursuit and feature sign algorithm is used to reconstruct the dirty image from the sparse spectrum and the noise is then removed by block-matching and 3-D filtering.