A New PSF Deconvolution Algorithm: Simultaneous Spatial Resolution Enhancement and Point Source Removal for Morphological Analysis of AGN Host Galaxies
Ren Kawase, Takatoshi Shibuya, Kazunori Matsuda

TL;DR
This paper introduces a novel PSF deconvolution algorithm that enhances galaxy image resolution and effectively removes AGN point sources, facilitating detailed morphological studies of distant galaxies.
Contribution
The proposed algorithm uniquely combines smooth, sparse, and point-source balance constraints to improve galaxy imaging and AGN removal.
Findings
Achieves spatial resolution comparable to Hubble Space Telescope.
Successfully removes bright central point sources from galaxy images.
Applicable to wide-field survey data for statistical morphological analysis.
Abstract
We propose a new point-spread function (PSF) deconvolution algorithm for images of galaxies hosting an active galactic nucleus (AGN), designed to simultaneously enhance the spatial resolution of the host galaxy and remove the bright central point source. In this algorithm, an intrinsic image is reconstructed by decomposing an observed image into two components: an image of an extended component (i.e., a host galaxy) and an image of a point-source component (i.e., an AGN). During image reconstruction, three constraints are imposed: (1) a smooth constraint on the image , which spatially smooths the host-galaxy structures; (2) a sparse constraint on the image , which localizes the point source to a small number of pixels; and (3) a new constraint, the point-source balance constraint, based on the pixel-wise product $I_{\rm sm} \times…
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