  Large-scale astronomical surveys can capture numerous images of celestial
objects, including galaxies and nebulae. Analysing and processing these images
can reveal intricate internal structures of these objects, allowing researchers
to conduct comprehensive studies on their morphology, evolution, and physical
properties. However, varying noise levels and point spread functions can hamper
the accuracy and efficiency of information extraction from these images. To
mitigate these effects, we propose a novel image restoration algorithm that
connects a deep learning-based restoration algorithm with a high-fidelity
telescope simulator. During the training stage, the simulator generates images
with different levels of blur and noise to train the neural network based on
the quality of restored images. After training, the neural network can directly
restore images obtained by the telescope, as represented by the simulator. We
have tested the algorithm using real and simulated observation data and have
found that it effectively enhances fine structures in blurry images and
increases the quality of observation images. This algorithm can be applied to
large-scale sky survey data, such as data obtained by LSST, Euclid, and CSST,
to further improve the accuracy and efficiency of information extraction,
promoting advances in the field of astronomical research.
