  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.
