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.
CITATION STYLE
Sakhaee, E., & Entezari, A. (2014). Learning splines for sparse tomographic reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8887, pp. 1–10). Springer Verlag. https://doi.org/10.1007/978-3-319-14249-4_1
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