FALCON: Unfolded Variational Model for Blind Deconvolution and Segmentation in 3d Dental Imaging
Résumé
This paper proposes an unfolded variational network for joint 3D blind deconvolution and segmentation, derived from a Mumford-Shah (MS) estimation. The approach unrolls a Split Bregman optimization into a fixed number of learnable iterations, effectively combining model-driven regularization with data-driven parameter adaptation. When applied to Cone Beam Computed Tomography (CBCT) volumes, the approach restores fine anatomical details approaching micro-CT (µ-CT) quality while maintaining its interpretability through explicit variational terms. A subsequent clustering-based segmentation further refines the reconstructed structures, highlighting the potential of hybrid unfolding schemes to integrate physics-based priors and deep learning (DL) representations in 3D medical imaging.
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