  In the imaging process of an astronomical telescope, the deconvolution of its
beam or Point Spread Function (PSF) is a crucial task. However, deconvolution
presents a classical and challenging inverse computation problem. In scenarios
where the beam or PSF is complex or inaccurately measured, such as in
interferometric arrays and certain radio telescopes, the resultant blurry
images are often challenging to interpret visually or analyze using traditional
physical detection methods. We argue that traditional methods frequently lack
specific prior knowledge, thereby leading to suboptimal performance. To address
this issue and achieve image deconvolution and reconstruction, we propose an
unsupervised network architecture that incorporates prior physical information.
The network adopts an encoder-decoder structure while leveraging the
telescope's PSF as prior knowledge. During network training, we introduced
accelerated Fast Fourier Transform (FFT) convolution to enable efficient
processing of high-resolution input images and PSFs. We explored various
classic regression networks, including autoencoder (AE) and U-Net, and
conducted a comprehensive performance evaluation through comparative analysis.
