http://arxiv.org/abs/2211.01567
Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning. We introduce a so-called “physics-based deep learning” approach to the Point Spread Function (PSF) deconvolution problem in galaxy surveys. We apply algorithm unrolling and the Plug-and-Play technique to the Alternating Direction Method of Multipliers (ADMM) with a Poisson noise model and use a neural network to learn appropriate priors from simulated galaxy images. We characterise the time-performance trade-off of several methods for galaxies of differing brightness levels, showing an improvement of 26% (SNR=20)/48% (SNR=100) compared to standard methods and 14% (SNR=20) compared to modern methods.
T. Li and E. Alexander
Fri, 4 Nov 22
6/84
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