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Title: Implementing Automatic Denoising Techniques in an Iterative Finite Element based Digital Image Correlation Analysis.

Abstract: The ability to measure displacement and determine strain through digital cameras is of great interest to the computational mechanics community. Digital Image Correlation (DIC) is a non-contact, non-destructive, full-field measurement technique that has been widely adopted for its ability to measure complex displacement fields with high accuracy. This work proposes novel approaches to DIC methodology and the ability to handle relatively large displacements. The first approach examined is an iterative DIC technique designed to decompose large deformations into smaller, incremental steps. The second approach evaluated is a robust principal component analysis (RPCA)-based automatic denoising and smoothing algorithm to post-process the DIC results, enhancing the data quality in preparation for the subsequent strain calculations. Finally, a combined method of integrating both techniques is explored to mitigate noise propagation throughout the iterative analysis. This work demonstrates the potential of this proposed iDIC-RPCA method to determine large and complex displacement fields.