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Prior Image Induced Regularization Method for Electrical Capacitance Tomography
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DOI：10.1109/ACCESS.2018.2886239.png)
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Abstract
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The image reconstruction is a crucial step in the electrical capacitance tomography. This paper presents a new methodology for improving the reconstruction accuracy. The prior image induced regularization from the deep convolutional extreme learning machine (DCELM) is introduced, which is integrated with the domain knowledge related to imaging targets to form a more effective mathematical model for reconstruction. A new numerical scheme is developed to train the DCELM more effectively. The fast iterative shrinkage-thresholding method is embedded into the alternating direction method of multipliers (ADMM) to form a new solver for the proposed imaging model. Extensive validations are implemented to evaluate the proposed imaging method. The numerical results demonstrate that the proposed imaging technique outperforms the state-of-the-art reconstruction methods and produces better reconstructions.
Keywords:
Prior image induced regularization
image reconstruction
inverse problem
deep convolutional extreme learning machine
iterative imaging method
electrical capacitance tomography
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