iterative image reconstruction
learned optimization algorithm
Recently, an extended family of power-divergence measures with two parameters was proposed together with an iterative reconstruction algorithm based on minimization of the divergence measure as an objective function of the reconstructed images for computed tomography. Numerical experiments on the reconstruction algorithm illustrated that it has advantages over conventional iterative methods from noisy measured projections by setting appropriate values of the parameters. In this paper, we present a novel neural network architecture for determining the most appropriate parameters depending on the noise level of the projections and the shape of the target image. Through experiments, we show that the algorithm of the architecture, which has an optimization sub-network with multiplicative connections rather than additive ones, works well.
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