Solar Image Deconvolution by Generative Adversarial Network

Autor:	Xu, Long, Sun, Wenqing, Yan, Yihua, Zhang, Weiqiang
Rok vydání:	2020
Předmět:	Astrophysics - Solar and Stellar Astrophysics Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
DOI:	10.1088/1674-4527/20/11/170
Popis:	With Aperture synthesis (AS) technique, a number of small antennas can assemble to form a large telescope which spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a single-dish antenna. Different from direct imaging system, an AS telescope captures the Fourier coefficients of a spatial object, and then implement inverse Fourier transform to reconstruct the spatial image. Due to the limited number of antennas, the Fourier coefficients are extremely sparse in practice, resulting in a very blurry image. To remove/reduce blur, "CLEAN" deconvolution was widely used in the literature. However, it was initially designed for point source. For extended source, like the sun, its efficiency is unsatisfied. In this study, a deep neural network, referring to Generative Adversarial Network (GAN), is proposed for solar image deconvolution. The experimental results demonstrate that the proposed model is markedly better than traditional CLEAN on solar images. Comment: 14 pages, 6 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.03850 Zobrazit plný text záznamu View this record from Arxiv