Solar Image Processing Based On Compressive Sensing

Solar radio imaging relies on deconvolution algorithms to offset sparse sampling in the Fourier plane.There are two traditional solar radio reconstruction algorithms: the CLEAN algorithm and the maximum entropy algorithm.These two algorithms have good reconstruction effects for point sources and spread sources,respectively,but neither method essentially solves the sparse sampling problem.Based on this,combined with the principle of synthetic aperture radio observation and compressed sensing imaging technology,this paper studies a new solar radio image reconstruction algorithm.The main contents of this thesis are as follows:(1)This paper investigates the development status of integrated aperture imaging technology and compressed sensing imaging technology,and analyzes the current development of compressed sensing technology for solar radio image reconstruction.(2)The three key parts of compressed sensing: sparse representation,measurement matrix,and reconstruction algorithm are described in detail.(3)Research on the sparse representation of solar images based on adaptive dictionary:In this paper,the sparse representation method of the sun image is studied in depth.The effects of the sparse representation of the sun image on the discrete cosine transform basis,the wavelet transform basis,and the K-SVD adaptive dictionary are analyzed from the theoretical proof and experimental verification.By using the block-trained K-SVD algorithm to train the dictionary,an adaptive dictionary of sparse representation of the sun image is obtained.The reconstruction of solar sparse representation images is performed.In view of the influence of image reconstruction algorithms on the image quality of solar sparse representations,the reconstruction performance of four greedy reconstruction algorithms is studied.(4)The sun image reconstruction algorithm based on compressed sensing is studied:The solar radio image reconstruction algorithms are studied: CLEAN algorithm,multi-scale CELAN algorithm and maximum entropy algorithm.Combining the principle of comprehensive aperture observation and compressed sensing imaging technology,a new solar radio image reconstruction algorithm is proposed,and the image reconstruction model of the algorithm in this paper is established.Because it is difficult to accurately know the sparseness of the solar radio image in advance,this paper improves the adaptability of the CoSaMP algorithm,improves the initial signal estimation,combines the idea of the SAMP algorithm,and uses the comparison of residual values as the termination condition of the algorithm to reduce the reconstructed image.The experimental results prove that the proposed algorithm has high reconstruction accuracy,strong anti-noise ability,and has the advantages of adaptive sparsity.Finally,compared with traditional reconstruction algorithms,the algorithm in this paper improves the quality of solar radio image reconstruction under certain conditions.The work in this paper is mainly based on the imaging of a new generation of centimeter-decimeter wave radio heliograph(Mingantu Spectral Radioheliograph,MUSER).