Simultaneous PSF Matching via Dual Kernel Optimization

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ID: 331900
2026
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Abstract
Abstract Standard point spread function (PSF) matching algorithms consider one image as the reference and aim to find a convolution kernel such that, when applied to the reference image, the PSFs of the reference and non-reference images are matched. This method works well if one image is a degraded version of the other. However, this is not always the case: if the PSFs of the two images have different orientations or shapes, a simple convolution of one of the images does not lead to a satisfactory match. While this can be trivially solved by convolving the first PSF with the second and vice-versa, this results in unnecessarily large PSFs. In this paper, we present a new algorithm, Dual Kernel Optimization (DKO), that simultaneously solves for two convolution kernels, one for each image, such that the PSFs match and the sizes of the kernels are minimized. This results in a minimally-degraded final set of PSFs. Since this problem is highly degenerate, we use Stochastic Gradient Langevin Dynamics (SGLD) which rigorously explores the parameter space and converges on the globally-optimal solution. We discuss the algorithmic challenges for this problem and the modifications to standard SGLD used to constrain it. Finally, we include a discussion on the application of this methodology to real-world images, including cases where the PSF varies over the field of view.
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openalex_W7214304806 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Carter Lee Rhea, Pieter Van Dokkum, Roberto Abraham, Imad Pasha, Steven R. Janssens, William P. Bowman, Deborah Lokhorst, Seery Chen, Qing Lui
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Journal RAS Techniques and Instruments
Year 2026
DOI
10.1093/rasti/rzag077
URL
https://doi.org/10.1093/rasti/rzag077
Keywords Keywords not found

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