Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks [article]

Joseph Shtok, Michael Zibulevsky, Michael Elad
<span title="2013-11-28">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography. Our technique is based on a local nonlinear fusion of several image estimates, all obtained by applying a chosen reconstruction algorithm with different values of its control parameters. Usually such output images have different bias/variance trade-off. The fusion of the images is performed by
more &raquo; ... d neural network trained on a set of known examples. Numerical experiments show an improvement in reconstruction quality relatively to existing direct and iterative reconstruction methods.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1311.7251v1</a> <a target="_blank" rel="external noopener" href="">fatcat:o2bkjflp6ngttcx3gzreju3ayq</a> </span>
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