Table of Contents
J. Imaging, Volume 4, Issue 11 (November 2018) – 13 articles
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Cover Story (view full-size image) In this paper, we present the use of mixed-scale dense convolutional neural networks to improve [...] Read more. In this paper, we present the use of mixed-scale dense convolutional neural networks to improve tomographic reconstruction from limited data. Standard reconstruction algorithms tend to produce inaccurate images when presented with limited data, or have prohibitively high computational costs. Existing machine learning approaches have promising results for improving image quality, but have several disadvantages when applied to large tomographic images. Mixed-scale dense networks are specifically designed to avoid these disadvantages, and in this paper we show that they can significantly improve reconstruction quality for various types of data limitations compared with existing algorithms. View this paper.