PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909126780518400 |
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| author | Ni, Shulei Qiu, Yisheng Chen, Yunchun Song, Zihao Chen, Hao Jiang, Xuejian Chen, Huaxi |
| author_facet | Ni, Shulei Qiu, Yisheng Chen, Yunchun Song, Zihao Chen, Hao Jiang, Xuejian Chen, Huaxi |
| contents | In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task. However, deconvolution presents a classical and challenging inverse computation problem. In scenarios where the beam or PSF is complex or inaccurately measured, such as in interferometric arrays and certain radio telescopes, the resultant blurry images are often challenging to interpret visually or analyze using traditional physical detection methods. We argue that traditional methods frequently lack specific prior knowledge, thereby leading to suboptimal performance. To address this issue and achieve image deconvolution and reconstruction, we propose an unsupervised network architecture that incorporates prior physical information. The network adopts an encoder-decoder structure while leveraging the telescope's PSF as prior knowledge. During network training, we introduced accelerated Fast Fourier Transform (FFT) convolution to enable efficient processing of high-resolution input images and PSFs. We explored various classic regression networks, including autoencoder (AE) and U-Net, and conducted a comprehensive performance evaluation through comparative analysis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_01692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution Ni, Shulei Qiu, Yisheng Chen, Yunchun Song, Zihao Chen, Hao Jiang, Xuejian Chen, Huaxi Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Computer Vision and Pattern Recognition Image and Video Processing In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task. However, deconvolution presents a classical and challenging inverse computation problem. In scenarios where the beam or PSF is complex or inaccurately measured, such as in interferometric arrays and certain radio telescopes, the resultant blurry images are often challenging to interpret visually or analyze using traditional physical detection methods. We argue that traditional methods frequently lack specific prior knowledge, thereby leading to suboptimal performance. To address this issue and achieve image deconvolution and reconstruction, we propose an unsupervised network architecture that incorporates prior physical information. The network adopts an encoder-decoder structure while leveraging the telescope's PSF as prior knowledge. During network training, we introduced accelerated Fast Fourier Transform (FFT) convolution to enable efficient processing of high-resolution input images and PSFs. We explored various classic regression networks, including autoencoder (AE) and U-Net, and conducted a comprehensive performance evaluation through comparative analysis. |
| title | PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2403.01692 |