A symmetry concept and significance of fringe patterns as a direct diagnostic tool in artwork conservation

Abstract

Coherent metrology has gained great importance as a branch of highly accurate, non-destructive, precise measurement technology for industrial, aeronautic, medical, and other applications. However, the fringe patterns produced seem to be enigmatic in terms of comprehension and analysis. Many laboratories are involved in improving hardware and software to keep unlocking the unique diagnostic capacities offered through the development of coherent metrology techniques. However, these advancements are not equally well suited for solving problems in all fields of application. The structural diagnosis of artwork is a distinctive field to which coherent metrology is applied. Works of art are unique constructions for which there are strict handling and moral rules aimed at their preservation. Fringe pattern evaluation provides much information about the condition of artwork being investigated, and establishing fringe patterns is one of the most efficient structural deformation and defect detection diagnostic tools. Previous collaborative studies have shown the main fringe patterns and their typical classification with regard to defects. Nevertheless, the complexity of the results prevents defect detection automation based on a fringe pattern classification table. The use of fringe patterns for the structural diagnosis of artwork is important for conveying crucial detailed information and dense data sources that are unmatched compared to those obtained using other conventional or modern techniques. Hologram interferometry fringe patterns uniquely reveal existing and potential structural conditions independent of object shape, surface complexity, material inhomogeneity, and multilayered and mixed media structures, without requiring contact and interaction with the precious surface. Thus, introducing a concept that allows fringe patterns to be considered as a powerful standalone physical tool for direct structural condition evaluation with a focus on artwork conservators’ need for structural diagnosis is crucial. The gravity of this aim intensifies when the particularities of ethics and safety in the field of art conservation are considered. There are ways to obtain the advantages of fringe patterns even when specialized software and advanced analysis algorithms fail to convey usable information. Interactively treating the features of fringe patterns through step-wise reasoning formulates the knowledge basis to automate defect isolation and identification procedures for machine learning and artificial intelligence (AI) development. The transfer of understanding of the significance of these fringe patterns to an AI system through logical steps is this work’s ultimate technical aim. Research on this topic is ongoing.