20 March 2015 A primal dual fixed point algorithm for constrained optimization problems with applications to image reconstruction
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Abstract
Computed tomography (CT) image reconstruction problems can be solved by finding the minimization of a suitable objective function. The first-order methods for image reconstruction in CT have been popularized in recent years. These methods are interesting because they need only the first derivative information of the objective function and can solve non-smooth regularization functions. In this paper, we consider a constrained optimization problem which often appeared in the CT image reconstruction problems. For the unconstrained case, it has been studied recently. We dedicate to propose an efficient algorithm to solve the constrained optimization problem. Numerical experiments to image reconstruction benchmark problem show that the proposed algorithms can produce better reconstructed images in signal-to-noise than the original algorithm and other state-of-the-art methods.
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Yuchao Tang, Yuchao Tang, } "A primal dual fixed point algorithm for constrained optimization problems with applications to image reconstruction", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94131W (20 March 2015); doi: 10.1117/12.2081607; https://doi.org/10.1117/12.2081607
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