Noise reduction on single-shot images using an autoencoder

Abstract

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.

arXiv comment13 pages, 6 figures, 6 tables, Accepted for publication in MNRAS

References in corpus (22)

• Methods for Interpreting and Understanding Deep Neural Networks
• Cosmic-Ray Rejection by Laplacian Edge Detection
• A New Non-Parametric Approach to Galaxy Morphological Classification
• A New Approach to Galaxy Morphology: I. Analysis of the Sloan Digital Sky Survey Early Data Release
• Science with ASKAP - the Australian Square Kilometre Array Pathfinder
• Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
• Pan-STARRS Pixel Processing: Detrending, Warping, Stacking
• A Fast Algorithm for Cosmic Rays Removal from Single Images
• Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
• Machine learning for transient discovery in Pan-STARRS1 difference imaging
• Learning to Denoise Astronomical Images with U-nets
• Cosmic Ray Rejection by Linear Filtering of Single Images
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• Denoising of gravitational wave signals via dictionary learning algorithms
• Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method
• A catalogue of structural and morphological measurements for DES Y1
• Evaluation of cosmic ray rejection algorithms on single-shot exposures
• The impact from survey depth and resolution on the morphological classification of galaxies
• Classification of gravitational-wave glitches via dictionary learning
• Optical Transient Object Classification in Wide Field Small Aperture Telescopes with Neural Networks
• A comparative analysis of denoising algorithms for extragalactic imaging surveys
• JPEG2000 Image Compression on Solar EUV Images

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