Non-convex blind deconvolution approach for sparse radio-interferometric imaging

Audrey Repetti, Jasleen Birdi, Yves Wiaux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

New generations of imaging devices aim to produce high resolution and high dynamic range images. In this context, the associated high dimensional inverse problems can become extremely challenging from an algorithmic view point. In addition, the imaging procedure can be affected by unknown calibration kernels. This leads to the need of performing joint image reconstruction and calibration, and thus of solving non-convex blind deconvolution problems. In this work, we focus on the joint calibration and imaging problem in radio-interferometric imaging in astronomy. To solve this problem, we leverage a block coordinate forward-backward algorithm, specifically designed to minimize non-smooth non-convex and high dimensional objective functions.We demonstrate by simulation the performance of this first joint imaging and calibration method in radio-interferometry.
Original languageEnglish
Title of host publication6th Signal Processing with Adaptive Sparse Structured Representations workshop 2017
Pages1-2
Number of pages2
Publication statusPublished - 5 Jun 2017
Event6th Signal Processing with Adaptive Sparse Structured Representations workshop 2017 - Lisbon, Portugal
Duration: 5 Jun 20178 Jun 2017

Workshop

Workshop6th Signal Processing with Adaptive Sparse Structured Representations workshop 2017
Abbreviated titleSPARS 2017
CountryPortugal
CityLisbon
Period5/06/178/06/17

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