| Literature DB >> 28855745 |
Ozan Öktem1, Chong Chen2, Nevzat Onur Domaniç3, Pradeep Ravikumar3, Chandrajit Bajaj3.
Abstract
We introduce a reconstruction framework that can account for shape related a priori information in ill-posed linear inverse problems in imaging. It is a variational scheme that uses a shape functional defined using deformable templates machinery from shape theory. As proof of concept, we apply the proposed shape based reconstruction to 2D tomography with very sparse measurements, and demonstrate strong empirical results.Entities:
Keywords: 37C05; 44A12; 47A52; 54C56; 57N25; 57R27; 65F22; 65R30; 65R32; 92C55; 94A08; 94A12; Tomography; electron tomography; inverse problems; reconstruction; regularization; shape analysis
Year: 2017 PMID: 28855745 PMCID: PMC5573282 DOI: 10.1088/1361-6420/aa55af
Source DB: PubMed Journal: Inverse Probl ISSN: 0266-5611 Impact factor: 2.407