  Environmental and instrumental conditions can cause anomalies in astronomical
images, which can potentially bias all kinds of measurements if not excluded.
Detection of the anomalous images is usually done by human eyes, which is slow
and sometimes not accurate. This is an important issue in weak lensing studies,
particularly in the era of large scale galaxy surveys, in which image qualities
are crucial for the success of galaxy shape measurements. In this work we
present two automatic methods for detecting anomalous images in astronomical
datasets. The anomalous features can be divided into two types: one is
associated with the source images, and the other appears on the background. Our
first method, called the Entropy Method, utilizes the randomness of the
orientation distribution of the source shapes and the background gradients to
quantify the likelihood of an exposure being anomalous. Our second method
involves training a neural network (autoencoder) to detect anomalies. We
evaluate the effectiveness of the Entropy Method on the CFHTLenS and DECaLS DR3
data. In CFHTLenS, with 1171 exposures, the Entropy Method outperforms human
inspection by detecting 12 of the 13 anomalous exposures found during human
inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the
Entropy method detects a significant number of anomalous exposures while
keeping a low false positive rate. We find that although the neural network
performs relatively well in detecting source anomalies, its current performance
is not as good as the Entropy Method.
