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Regularization of the level-set (LS) field is a critical part of LS-based topology optimization (TO) approaches. Traditionally this is achieved by advancing the LS field through the solution of a Hamilton-Jacobi equation combined with a reinitialization scheme. This approach, however, may limit the maximum step size and introduces discontinuities in the design process. Alternatively, energy functionals...
The discrete tomographic reconstruction problem is generally considered for binary image. In this work, we consider the reconstruction of an image with more than two grey levels and compare two reconstruction methods. The first one is based on a classical TV regularization and the second one is a level-set regularization method. In this second method, the discrete tomographic problem is formulated...
In this work, we propose a stochastic level-set method to reconstruct binary tomography cross-sections from a small number of projections. A first reconstruction image is obtained with a deterministic level-set regularization method. The reconstruction is then refined with a stochastic partial differential equation based on a Stratanovitch formulation. The methods are tested on a slice of bone microstructure...
In this work, we study a new reconstruction method for binary tomography cross-sections of the bone microstructure. The binary cross-sections are obtained from a small number of projections and the binary tomography problem is formulated as a non linear inverse problem. Stable solution are obtained with level-set methods and a Bounded Variation-Sobolev regularization of the level set function. The...
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