Super-resolution ultrasonic Lamb wave imaging via dispersion-embedded deconvolution

Enhancing the spatial resolution of ultrasonic Lamb wave imaging, fundamentally bounded by the diffraction limit, remains an important challenge in nondestructive testing (NDT). Seeking to exceed such a resolution limit, i.e., super -resolution (SR), is a difficult ill-posed inverse problem. While it may be directly and effectively interpreted as a deconvolution process, Lamb wave SR imaging is uniquely complicated by the significant dispersion nature of Lamb waves, which renders the system point spread function (PSF) inherently frequency-, mode-, and space-dependent. In this work, we develop a dispersion-informed deconvolution framework for SR imaging by explicitly embedding the Lamb-wave dispersion physics into the PSF modeling. Specifically, physically consistent PSF is incorporated into the total focusing forward model, enabling inversion of dispersion-induced blurring and recovery of structural details that are irreversibly lost in conventional array images. Validations are performed through finite element simulations and laboratory experiments on aluminum plates. It is observed that the proposed framework achieves reliable super-resolution separation of closely-spaced defects beyond the classical diffraction limit while maintaining robustness under low signal-to-noise ratio (SNR) conditions. In particular, a comprehensive parametric study is conducted to elucidate the key factors governing its super-resolution capability. It is revealed that a larger effective aperture enhances spatial focusing and resolution, while denser sensor spacing improves PSF fidelity and inversion stability under dispersive propagation. Discussions including challenges identified in this study are presented.