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Large-scale astronomical surveys can capture numerous images of celestialobjects, including galaxies and nebulae. Analysing and processing these imagescan reveal intricate internal structures of these objects, allowing researchersto conduct comprehensive studies on their morphology, evolution, and physicalproperties. However, varying noise levels and point spread functions can hamperthe accuracy and efficiency of information extraction from these images. Tomitigate these effects, we propose a novel image restoration algorithm thatconnects a deep learning-based restoration algorithm with a high-fidelitytelescope simulator. During the training stage, the simulator generates imageswith different levels of blur and noise to train the neural network based onthe quality of restored images. After training, the neural network can directlyrestore images obtained by the telescope, as represented by the simulator. Wehave tested the algorithm using real and simulated observation data and havefound that it effectively enhances fine structures in blurry images andincreases the quality of observation images. This algorithm can be applied tolarge-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.

Image Restoration with Point Spread Function Regularization and Active Learning