Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction

• URL: http://arxiv.org/abs/2007.09522v1
• Date: Sat, 18 Jul 2020 21:53:27 GMT
• Title: Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction
• Authors: Xiajun Jiang, Sandesh Ghimire, Jwala Dhamala, Zhiyuan Li, Prashnna Kumar Gyawali, and Linwei Wang
• Abstract summary: We present a new approach to learn inverse imaging that exploit the underlying geometry and physics. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables. We then learn the geometry-dependent physics in between the two domains by explicitly modeling it via a bipartite graph.
• Score: 9.565653662306806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have shown great potential in image reconstruction problems in Euclidean space. However, many reconstruction problems involve imaging physics that are dependent on the underlying non-Euclidean geometry. In this paper, we present a new approach to learn inverse imaging that exploit the underlying geometry and physics. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables over their respective geometrical domains. We then learn the geometry-dependent physics in between the two domains by explicitly modeling it via a bipartite graph over the graphical embedding of the two geometry. We applied the presented network to reconstructing electrical activity on the heart surface from body-surface potential. In a series of generalization tasks with increasing difficulty, we demonstrated the improved ability of the presented network to generalize across geometrical changes underlying the data in comparison to its Euclidean alternatives.

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