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pro vyhledávání: '"Image deconvolution"'
Blind image deconvolution (BID) is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DIP) have motivated a series of DIP-based approaches, demonstrating remarkable success in BID. However, due to
Externí odkaz:
http://arxiv.org/abs/2407.14816
Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches that rely on analytical properties of the point spread function (PSF) to
Externí odkaz:
http://arxiv.org/abs/2405.16343
Publikováno v:
A&A 688, A6 (2024)
As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag
Externí odkaz:
http://arxiv.org/abs/2405.07842
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PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution
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
Externí odkaz:
http://arxiv.org/abs/2403.01692
In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling-based deep network design since ma
Externí odkaz:
http://arxiv.org/abs/2402.12872
Publikováno v:
The Astronomical Journal, Volume 168, Issue 2, id.55, 15 pp, 2024
We present STARRED, a Point Spread Function (PSF) reconstruction, two-channel deconvolution, and light curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted
Externí odkaz:
http://arxiv.org/abs/2402.08725
Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Classical iterative deconvolution approaches amplify noise and require careful parameter selection for an optimal trade-off between sharpness and grai
Externí odkaz:
http://arxiv.org/abs/2308.09426
Recovering clear images from blurry ones with an unknown blur kernel is a challenging problem. Deep image prior (DIP) proposes to use the deep network as a regularizer for a single image rather than as a supervised model, which achieves encouraging r
Externí odkaz:
http://arxiv.org/abs/2310.19477