  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.
