Estimated-PSF-based regularization method for spreading-effect suppression toward accurate defect sizing in coplanar capacitive sensing

Accurate defect size characterization is pivotal in safety-critical engineering applications, yet coplanar capacitive sensing NDE is limited by the inherent spreading effect that blurs and enlarges measured defect dimensions. Building on a previously proposed regularization-based deconvolution approach using an estimated point spread function (PSF), this study experimentally validates the method’s robustness and broad applicability across diverse coplanar capacitive sensor (CCS) geometries (triangular, square, rectangular, concentric, and annular), complex defect shapes, and engineering materials (CFRP and GFRP). Results demonstrate that the method consistently compensates for electrode-geometry-dependent spreading, restoring defect sizes to dimensions much closer to the true sizes, regardless of sensor geometry or defect shape. In particular, it suppresses anisotropic spreading in semi-conductive CFRP and restores spatial fidelity in isotropic GFRP, although the method’s performance is influenced by material properties. Notably, the estimated PSF—derived from a small circular defect—captures essential defect- and system-related information, enabling high-fidelity deconvolution across defect types and materials. Collectively, these findings advance coplanar capacitive sensing–based NDE toward accurate defect sizing, enabling reliable high-resolution imaging while supporting sensor comparison, multimodal data fusion, and deployment in safety-critical applications. The results establish regularization-based deconvolution as a key enabler for defect sizing accuracy in safety-critical engineering contexts, with future work focused on material-specific tuning and extension to more variable inspection conditions.