2020/09/11 by Jean‐Baptiste Courbot, Jean-Baptiste Courbot, Courbot, Jean-Baptiste +2
Computer Science · Engineering · Physics and Astronomy · #62H35 #Advanced Image Processing Techniques #Digital Holography and Microscopy #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Sparse and Compressive Sensing Techniques #cs.LG #eess.IV #electronic engineering #information engineering #msc:62H35
paper · pdf · doi:10.48550/arxiv.2009.05473
in Proceedings of iTWIST'20, Paper-ID: 08, Nantes, France, December, 2-4, 2020
arxiv created 2020/09/11 · openalex publication_date 2020/09/11 · arxiv updated 2020/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the context of gridless sparse optimization, the Sliding Frank Wolfe algorithm recently introduced has shown interesting analytical and practical properties. Nevertheless, is application to large data, such as in the case of 3D deconvolution, is computationally heavy. In this paper, we investigate a strategy for leveraging this burden, in order to make this method more tractable for 3D deconvolution. We show that a boosted SFW can achieve the same results in a significantly reduced amount of time.