With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluates image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point spread function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep learning based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.

翻译：随着天文数据量的不断增长，自动化数据处理流水线的需求日益增加，这类流水线能够无需人工干预地从观测数据中提取科学信息。其中关键的一环是图像质量评估与掩膜算法，该算法需综合考虑云层覆盖度、天空亮度、光学系统杂散光、点扩散函数大小与形状以及读出噪声等多种因素来评估图像质量，并时常需要对受噪声严重影响的区域进行掩膜处理。然而，现有算法往往仍需要大量人工干预，降低了数据处理效率。本研究提出一种基于深度学习的图像质量评估算法，利用自编码器学习高质量天文图像的特征。训练后的自编码器能够自动评估图像质量并对噪声影响区域进行掩膜处理。我们通过两个测试案例评估了算法性能：具有不同半高全宽点扩散函数的图像，以及具有复杂背景的图像。在第一个场景中，算法能够有效识别点扩散函数的变化，为测光提供有价值的参考信息；在第二个场景中，本方法能够成功掩膜受复杂区域影响的区域，显著提升测光精度。该算法可用于自动评估不同巡天项目获取的图像质量，进一步提高数据处理流水线的速度与鲁棒性。