A Multi-Resolution Approach to Complexity Reduction in Tomographic Reconstruction

Abstract

Most of the algorithms for tomographic reconstruction face the same problem: high computational complexity. In order to tackle this problem, this paper proposes a general multi-resolution approach that enables a flexible choice of reconstruction focus and thus saves computational power in reconstructions. The approach is demonstrated in this paper based on a reconstruction algorithm using a (improper) Markov random field prior with sparsifying NUV terms (nor-mal with unknown variance), where the unknown variances are learned by approximate EM (expectation maximization). The experimental and practical results show that both for simulated and real-world objects the proposed framework yields satisfying results with much lower computational cost.