Compressed sensing of radio heliograph and Gaussian denoising of dirty image

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.