  We present an application of autoencoders to the problem of noise reduction
in single-shot astronomical images and explore its suitability for upcoming
large-scale surveys. Autoencoders are a machine learning model that summarises
an input to identify its key features, then from this knowledge predicts a
representation of a different input. The broad aim of our autoencoder model is
to retain morphological information (e.g., non-parametric morphological
information) from the survey data whilst simultaneously reducing the noise
contained in the image. We implement an autoencoder with convolutional and
maxpooling layers. We test our implementation on images from the Panoramic
Survey Telescope and Rapid Response System (Pan-STARRS) that contain varying
levels of noise and report how successful our autoencoder is by considering
Mean Squared Error (MSE), Structural Similarity Index (SSIM), the second-order
moment of the brightest 20 percent of the galaxy's flux M20, and the Gini
coefficient, whilst noting how the results vary between the original images,
stacked images, and noise reduced images. We show that we are able to reduce
noice, over many different targets of observations, whilst retaining the
galaxy's morphology, with metric evaluation on a target by target analysis. We
establish that this process manages to achieve a positive result in a matter of
minutes, and by only using one single shot image compared to multiple survey
images found in other noise reduction techniques.
