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The reconstruction of X-Ray Ct images from low sampled data is of great interest in different applications, such as medicine or engineering. We follow a regularization approach with a smoothed differentiable Total Variation function . The problem is challenging for its very large size and because a good reconstruction is required in a very short time. We propose to use a gradient projection method, accelerated by exploiting a scaling strategy and generalized Barzilai-Borwein rules.
This presentation is part of Minisymposium “MS33 - Advances in reconstruction algorithms for computed tomography (4 parts)”
organized by: Gunay Dogan (Theiss Research, NIST) , Harbir Antil (George Mason University) , Elena Loli Piccolomini (Dept. Computer Science and Engineering, University of Bologna) , Samuli Siltanen (University of Helsinki) .