TL;DR
This paper introduces a marginal estimator for multi-object multi-frame blind deconvolution in solar imaging, improving robustness, regularization, and ease of implementation over traditional joint estimation methods.
Contribution
A novel marginal estimator that enhances regularization, contrast control, and simplifies implementation for blind deconvolution in solar imaging.
Findings
The method prevents noise from being misattributed to aberrations.
It is less sensitive to hyperparameters, enabling automatic tuning.
The approach is easily integrated into existing pipelines and open-source.
Abstract
High-resolution ground-based solar imaging relies heavily on multi-object multi-frame blind deconvolution to correct for atmospheric turbulence. However, the traditional joint maximum likelihood estimation methods in which object and the atmospheric aberrations are estimated together face some problems. In this paper, we introduce a marginal estimator for the multi-object multi-frame blind deconvolution problem. By employing a framework to marginalize over the observed objects, we develop a reconstruction method that offers several distinct advantages over joint estimation. First, the marginalization provides enhanced regularization that naturally accounts for object uncertainty, successfully preventing the reconstruction algorithm from erroneously assigning noise to high-order aberrations. Second, the marginal estimator yields more contrast control, as it is much less sensitive to the…
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