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.

中文翻译：

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生成对抗网络的太阳图像反卷积

利用孔径合成（AS）技术，许多小天线可以组合成一个大望远镜，其空间分辨率由两个最远天线的距离决定，而不是由单天线的直径决定。与直接成像系统不同，AS望远镜捕捉空间目标的傅里叶系数，然后进行傅里叶逆变换重建空间图像。由于天线数量有限，傅立叶系数在实践中非常稀疏，导致图像非常模糊。为了去除/减少模糊，“CLEAN”反卷积在文献中被广泛使用。但是，它最初是为点源设计的。对于像太阳这样的扩展源，其效率并不令人满意。在这项研究中，一个深度神经网络，指的是生成对抗网络（GAN），被提议用于太阳图像去卷积。实验结果表明，所提出的模型在太阳图像上明显优于传统的 CLEAN。