ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

ASCL Code Record

[ascl:1906.015] OIT: Nonconvex optimization approach to optical-interferometric imaging

In the context of optical interferometry, only undersampled power spectrum and bispectrum data are accessible, creating an ill-posed inverse problem for image recovery. Recently, a tri-linear model was proposed for monochromatic imaging, leading to an alternated minimization problem; in that work, only a positivity constraint was considered, and the problem was solved by an approximated Gauss–Seidel method.

The Optical-Interferometry-Trilinear code improves the approach on three fundamental aspects. First, the estimated image is defined as a solution of a regularized minimization problem, promoting sparsity in a fixed dictionary using either an l1 or a (re)weighted-l1 regularization term. Second, the resultant non-convex minimization problem is solved using a block-coordinate forward–backward algorithm. This algorithm is able to deal both with smooth and non-smooth functions, and benefits from convergence guarantees even in a non-convex context. Finally, the model and algorithm are generalized to the hyperspectral case, promoting a joint sparsity prior through an l2,1 regularization term.

Code site:
https://github.com/basp-group/Optical-Interferometry-Trilinear https://basp-group.github.io/Optical-Interferometry-Trilinear/
Described in:
https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.1142B
Bibcode:
2019ascl.soft06015B

Views: 272

ascl:1906.015
Add this shield to your page
Copy the above HTML to add this shield to your code's website.