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Optical Tomography Based on a Nonlinear Model that Handles Multiple Scattering

M.H. Shoreh, A. Goy, J. Lim, U. Kamilov, M. Unser, D. Psaltis

Proceedings of the Forty-Second IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'17), New Orleans LA, USA, March 5-9, 2017, pp. 6220-6224.



Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is generally nonconvex, the solution it obtains is not guaranteed to be globally optimal. In this paper, we describe linear and nonlinear tomographic reconstruction methods and compare them numerically. We present a review of the LT and, in addition, we investigate the influence of the initialization and exemplify the effect of regularization on the convergence of the algorithm. In particular, we show that both are essential for high-quality imaging in strongly scattering scenarios.


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