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Learning Splines for Sparse Tomographic Reconstruction

  • Elham Sakhaee
  • Alireza Entezari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8887)

Abstract

In a few-view or limited-angle computed tomography (CT), where the number of measurements is far fewer than image unknowns, the reconstruction task is an ill-posed problem. We present a spline-based sparse tomographic reconstruction algorithm where content-adaptive patch sparsity is integrated into the reconstruction process. The proposed method leverages closed-form Radon transforms of tensor-product B-splines and non-separable box splines to improve the accuracy of reconstruction afforded by higher order methods. The experiments show that enforcing patch-based sparsity, in terms of a learned dictionary, on higher order spline representations, outperforms existing methods that utilize pixel-basis for image representation as well as those employing wavelets as sparsifying transform.

Keywords

Sparse Representation Tomographic Reconstruction Dictionary Learning Orthogonal Match Pursuit Projection Angle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Elham Sakhaee
    • 1
  • Alireza Entezari
    • 1
  1. 1.CISE DepartmentUniversity of FloridaGainesvilleUSA

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