Signal-quality adaptive fusion algorithm based on white-light interferometry for 3D characterization of
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Micro-structured functional surfaces are material interfaces engineered with geometric features across macro- to micro-scales to achieve tailored functional properties. Above varieties of functional applications, superhydrophobic metallic surfaces have garnered significant interest for their exceptional water repellency, which enables applications in self-cleaning, anti-icing, drag reduction, and anti-biofouling across fields such as precision manufacturing, aerospace, and biomedicine. Their functional performance is critically dependent on the precise 3D topography of their micro-structures. These structures, characterized by high local slopes and significant roughness, pose a critical challenge to high-resolution, nondestructive 3D measurement. This study addresses the signal distortion and accuracy degradation in white-light scanning interferometry (WLSI) when applied to superhydrophobic metallic surfaces. We propose a signal-quality adaptive fusion algorithm that assesses and corrects interference signals based on their intrinsic quality. The algorithm integrates focus-measure reconstruction with correlation analysis for adaptive signal processing. To validate the proposed algorithm, experiments employed a superhydrophobic metallic surface fabricated by a femtosecond laser. The results demonstrate that the measurement accuracy improves by more than two times compared to conventional methods. Furthermore, the reconstructed 3D topography shows excellent agreement with commercial profilometer data in valid regions and successfully recovers missing morphological information in steep areas, confirming its combined advantages in measurement accuracy and data completeness.
