Abstract With aperture synthesis (AS) technique, a number of small antennas can be assembled to form a large telescope whose spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a single-dish antenna. In contrast from a 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 has been widely used in the literature. However, it was initially designed for a point source. For an extended source, like the Sun, its efficiency is unsatisfactory. 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. The main purpose of this work is visual inspection instead of quantitative scientific computation. We believe that this will also help scientists to better understand solar phenomena with high quality images.
Keywords deep learning (DL) — generative adversarial network (GAN) — solar radio astronomy — image reconstruction — aperture synthesis
It accepts original submissions from all over the world and is internationally published and distributed by IOP