Point Spread Function Deconvolution Using a Convolutional Autoencoder for Astronomical Applications
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866913524104560640 |
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| author | Sreejith, Sreevarsha Slosar, Anže Wang, Hong |
| author_facet | Sreejith, Sreevarsha Slosar, Anže Wang, Hong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_19605 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Point Spread Function Deconvolution Using a Convolutional Autoencoder for Astronomical Applications Sreejith, Sreevarsha Slosar, Anže Wang, Hong Instrumentation and Methods for Astrophysics Astrophysics of Galaxies 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. |
| title | Point Spread Function Deconvolution Using a Convolutional Autoencoder for Astronomical Applications |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2310.19605 |