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An optimisation method for coplanar array capacitor image reconstruction based on Chambolle-Pock framework with automatic parameter selection
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AI
DOI：10.1080/10589759.2024.2436116.png)
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Abstract
En 中文
Coplanar array capacitance imaging is an attractive imaging technique in industrial and detection fields, but low-quality images reduce its practicality and reliability. The inverse problem is difficult to solve and the image reconstruction quality is low, in this paper, an auxiliary optimisation strategy is proposed to transform the inverse problem of image reconstruction into a general minimisation problem, combined with the threshold of automatic parameter selection for feature extraction of capacitive data. The Kullback-Leibler(KL) data error term is combined with the Total Variation (TV) semi-norm to form a new imaging model. Utilising the Chambolle-Pock framework, the image reconstruction problem is transformed into a general minimisation problem to obtain high-quality images. Capacitance data from a 3 $ \times $x 4 coplanar array capacitance sensor is used for feature extraction, serving as the basis for automatic parameter selection in the auxiliary optimisation strategy. The proposed Chambolle-Pock framework with the support of the auxiliary optimisation strategy, validated through simulation and experiments, outperforms popular imaging algorithms in terms of performance. The novel automatic parameter selection optimization algorithm introduced in this paper enhances the accuracy of capacitor image reconstruction in coplanar arrays.
Keywords:
Planar array capacitance imaging
KL-TV
Chambolle-Pock framework
feature extraction
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