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arXiv · 2310.19605

Point Spread Function Deconvolution Using a Convolutional Autoencoder for Astronomical Applications

Sreevarsha Sreejith·Anže Slosar·Hong Wang
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Abstract

A major issue in optical astronomical image analysis is the combined effect of the instrument's point spread function (PSF) and the atmospheric seeing that blurs images and changes their shape in a way that is band and time-of-observation dependent. In this work we present a very simple neural network based approach to non-blind image deconvolution that relies on feeding a Convolutional Autoencoder (CAE) input images that have been preprocessed by convolution with the corresponding PSF and its regularized inverse, a method which is both conceptually simple and computationally less intensive. We also present here, a new approach for dealing with limited input dynamic range of neural networks compared to the dynamic range present in astronomical images.

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Sreevarsha Sreejith, Anže Slosar, Hong Wang. 2023-10-30. Point Spread Function Deconvolution Using a Convolutional Autoencoder for Astronomical Applications. https://arxiv.org/abs/2310.19605

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