{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iteratively-reweighted-least-squares-for-1","title":"Iteratively Reweighted Least Squares for Phase Unwrapping","arxiv_id":"2401.09961","date":"2024-01-18","proceeding":null,"authors":["Benjamin Dubois-Taine","Roland Akiki","Alexandre d'Aspremont"],"abstract":"The 2D phase unwrapping problem seeks to recover a phase image from its observation modulo 2$\\pi$, and is a crucial step in a variety of imaging applications. In particular, it is one of the most time-consuming steps in the interferometric synthetic aperture radar (InSAR) pipeline. In this work we tackle the $L^1$-norm phase unwrapping problem. In optimization terms, this is a simple sparsity-inducing problem, albeit in very large dimension. To solve this high-dimensional problem, we iteratively solve a series of numerically simpler weighted least squares problems, which are themselves solved using a preconditioned conjugate gradient method. Our algorithm guarantees a sublinear rate of convergence in function values, is simple to implement and can easily be ported to GPUs, where it significantly outperforms state of the art phase unwrapping methods.","url_abs":"https://arxiv.org/abs/2401.09961v1","url_pdf":"https://arxiv.org/pdf/2401.09961v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"iteratively-reweighted-least-squares-for-1","repo_url":"https://github.com/bpauld/phaseunwrapping","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}