基于幅度的CLEAN天文图像重建算法
An Amplitude-Based CLEAN Astronomical Image Reconstruction Algorithm
DOI: 10.12677/AAM.2021.104121, PDF,    国家自然科学基金支持
作者: 张 利*：贵州大学大数据与信息工程学院，贵州 贵阳；黔南民族师范学院，贵州 都匀；西华师范大学计算机学院，四川 南充；卫星奇, 卢 梅, 肖一凡, 王 蓓, 谢 泉：贵州大学大数据与信息工程学院，贵州 贵阳；米立功：贵州大学大数据与信息工程学院，贵州 贵阳；黔南民族师范学院，贵州 都匀；贺春林：西华师范大学计算机学院，四川 南充
关键词: 干涉阵成图；VLBI成图；CLEAN算法；CASA；Interferometric Imaging； VLBI Imaging； CLEAN Algorithm； CASA
摘要: 射电干涉成像的质量部分依赖于图像重建算法。CLEAN算法是一种应用非常广泛的射电天文数据的重建算法，已经成为射电天文干涉数据处理软件的标准配置。典型的CLEAN算法使用delta函数集合来逼近我们真实的天文图像，然而当前没有工作来优化这种集合，从而达到提高图像重建效果的目的。本文改进了当前的CLEAN算法，提出了一种基于幅度的重建算法。通过通用天文软件程序包(Common Astronomy Software Applications, CASA)模拟美国甚大干涉阵(VLA)进行了观测，对得到测量数据MS (Measurement Sets)使用常规的CLEAN算法和本文改进的CLEAN算法进行了重建。实验显示本文改进的算法的重建图像有更高动态范围。
Abstract: The quality of radio interference imaging partly depends on the image reconstruction algorithm. The CLEAN algorithm is a widely used reconstruction algorithm for radio astronomy data and has become the standard configuration of radio interferometric data processing software. A typical CLEAN algorithm uses a set of delta functions to approximate a real astronomical image. However, currently there is no work to optimize such a set to achieve the purpose of improving the image reconstruction effect. This paper improves the current CLEAN algorithm and proposes an amplitude-based reconstruction algorithm. The VLA observation was simulated by the Common Astronomy Software Applications (CASA), and the conventional CLEAN algorithm and the improved CLEAN algorithm were used to reconstruct the measured data MS (Measurement Sets). Experiments show that the improved algorithm has a higher dynamic range of the reconstructed image.
文章引用：张利, 卫星奇, 卢梅, 肖一凡, 米立功, 贺春林, 王蓓, 谢泉. 基于幅度的CLEAN天文图像重建算法[J]. 应用数学进展, 2021, 10(4): 1115-1121. https://doi.org/10.12677/AAM.2021.104121

参考文献

[1] Thompson, A.R., Moran, J.M. and Swenaon, G.W. (2017) Interferometry and Synthesis in Radio Astronomy. 3rd Edition, Springer Nature, Cham. [Google Scholar] [CrossRef] 
[2] Starck, J.L., Pantin, E. and Murtagh, F. (2002) Deconvolution in Astronomy: A Review. PASP, 114, 1051S. [Google Scholar] [CrossRef] 
[3] Candès, E.J. (2006) Compressive Sampling. Proceedings of the International Congress of Mathematicians, Madrid, 22-30 August 2006, 1433-1452. [Google Scholar] [CrossRef] 
[4] Wiaux, Y., Jacques, L., Puy, G., Scaife, A.M.M. and Vandergheynst, P. (2009) Compressed Sensing Imaging Techniques for Radio Interferometry. MNRAS, 395, 1733W. [Google Scholar] [CrossRef] 
[5] Carrillo, R.E., McEwen, J.D. and Wiaux, Y. (2012) Sparsity Averaging Reweighted Analysis (SARA): A Novel Algorithm for Radio-Interferometric Imaging. MNRAS, 426, 1223C. [Google Scholar] [CrossRef] 
[6] Li, F., Cornwell, T.J. and Hoog, F. (2011) The Application of Compressive Sampling to Radio Astronomy I: Deconvolution. Astronomy and Astrophysics, 528, 31. [Google Scholar] [CrossRef] 
[7] Högbom, J.A. (1974) Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines. A & ASS, 15, 417.
[8] Schwarz, U.J. (1987) Mathematical-Statistical Description of the Iterative Beam Removing Technique (Method CLEAN). Astronomy and Astrophysics, 65, 345-356.
[9] Clark, B.G. (1980) An Efficient Implementation of the Algorithm “CLEAN”. Astronomy and Astrophysics, 89, 377-378.
[10] Schwab, F.R. and Cotton, W.D. (1983) Global Fringe Search Techniques for VLBI. The Astronomical Journal, 88, 688S. [Google Scholar] [CrossRef] 
[11] Bhatnagar, S. and Cornwell, T.J. (2004) Scale Sensitive Deconvolution of Interferometric Images. I. Adaptive Scale Pixel (Asp) Decomposition. Astronomy and Astrophysics, 426, 747-754. [Google Scholar] [CrossRef] 
[12] Cornwell, T.J. (2008) Multiscale CLEAN Deconvolution of Radio Synthesis Images. IEEE Journal of Selected Topics in Signal Processing, 2, 793. [Google Scholar] [CrossRef] 
[13] Rau, U. and Cornwell, T.J. (2011) A Multi-Scale Multi-Frequency Deconvolution Algorithm for Synthesis Imaging in Radio Interferometry. Astronomy and Astrophysics, 532, A71. [Google Scholar] [CrossRef] 
[14] Zhang, L., Bhatnagar, S., Rau, U. and Zhang, M. (2016) Efficient Implementation of the Adaptive Scale Pixel Decomposition Algorithm. Astronomy and Astrophysics, 592, A128. [Google Scholar] [CrossRef] 
[15] Zhang, L., Zhang, M. and Liu, X. (2016) The Adaptive-Loop-Gain Adaptive-Scale CLEAN Deconvolution of Radio Interferometric Images. Astrophysics and Space Science, 361, 153. [Google Scholar] [CrossRef] 
[16] Cornwell, T.J. (2009) Hogbom’s CLEAN Algorithm. Impact on Astronomy and Beyond. Astronomy and Astrophysics, 500, 65. [Google Scholar] [CrossRef] 
[17] Heslop, D. and Dekkers, M.J. (2002) Spectral Analysis of Unevenly Spaced Climatic Time Series Using CLEAN: Signal Recovery and Derivation of Significance Levels Using a Monte Carlo Simulation. Physics of the Earth and Planetary Interiors, 130, 103-116. [Google Scholar] [CrossRef] 
[18] Ding, Y.R., Li, Z.Y. and Diwu, Y.Z. (1999) The CLEAN Method of Spectral Analysis and Its Application to the Oscillations of the Cataclysmic Variable TT Arietis. Chinese Astronomy and Astrophysics, 23, 484-492. [Google Scholar] [CrossRef] 
[19] Cornwell, T.J., Holdaway, M.A. and Uson, J.M. (1993) Radio-Interferometric Imaging of Very Large Objects: Implications for Array Design. Astronomy and Astrophysics, 271, 697C.

为你推荐