A MULTI-RESOLUTION APPROACH TO COMPLEXITY REDUCTION IN TOMOGRAPHIC RECONSTRUCTION

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 (normal 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.