Spectrally Sparse Tensor Reconstruction in Optical Coherence Tomography Using Nuclear Norm Penalisation

Mohamed Ibrahim Assoweh, Stéphane Chrétien and Brahim Tamadazte
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Mohamed Ibrahim Assoweh: Laboratoire de Mathématiques de Besançon, Université de Bourgogne-Franche Comté, 25030 Besançon, France
Stéphane Chrétien: Laboratoire ERIC, UFR ASSP, Université Lyon 2, 69676 Bron, France
Brahim Tamadazte: Institute for Intelligent Systems and Robotics, University of Sorbonne, CNRS, UMR 7222, 4 pl. Jussieu, 75005 Paris, France

Mathematics, 2020, vol. 8, issue 4, 1-31

Abstract: Reconstruction of 3D objects in various tomographic measurements is an important problem which can be naturally addressed within the mathematical framework of 3D tensors. In Optical Coherence Tomography, the reconstruction problem can be recast as a tensor completion problem. Following the seminal work of Candès et al., the approach followed in the present work is based on the assumption that the rank of the object to be reconstructed is naturally small, and we leverage this property by using a nuclear norm-type penalisation. In this paper, a detailed study of nuclear norm penalised reconstruction using the tubal Singular Value Decomposition of Kilmer et al. is proposed. In particular, we introduce a new, efficiently computable definition of the nuclear norm in the Kilmer et al. framework. We then present a theoretical analysis, which extends previous results by Koltchinskii Lounici and Tsybakov. Finally, this nuclear norm penalised reconstruction method is applied to real data reconstruction experiments in Optical Coherence Tomography (OCT). In particular, our numerical experiments illustrate the importance of penalisation for OCT reconstruction.

Keywords: tensor completion; tubal SVD; nuclear norm penalisation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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