A threshold optimization strategy for reducing the noise of co-planar array capacitive imaging

Abstract

Co-planar array capacitive imaging is a non-destructive testing technique based on the sensing of dielectric properties. As the number of independent electrodes in the sensor grows, handling noise becomes more and more critical. In this paper, numerical models and practical tests were used to explore the noise analysis and processing of the output signals of a 12-electrode co-planar array capacitive sensor. The effects of different geometric placements and distances on the output signal strength and sensitivity of electrode pairs were thoroughly assessed, revealing that distal interpolar spacing electrode pairs were particularly vulnerable to noise. As a consequence, a threshold optimization strategy was presented for decreasing the noise. Results revealed that threshold optimization efficiently reduced noise and emphasized the useful information for image reconstruction. The artifacts and deviations of the reconstructed images were reduced, and the structural similarity and Pearson product-moment correlation coefficients were boosted. The methodologies and findings may provide a reference for signal processing in co-planar array capacitive imaging.