Sound-Based Imaging Regularization Approaches in Near-field Acoustic Holography (2024)
Regularization of the inverse problem is a complex issue when using Near-field Acoustic Holography (NAH) techniques to identify vibrating sources. This article aims to compare and implement various regularization methods in the context of NAH. Specifically, it compares commonly used Tikhonov regularization, sparsity-based regularization, and neural networks (NN) regularization for a planar NAH array with measurements obtained from an experimental setup. Additionally, it theoretically introduces Green’s function-based regularization. The first three types of regularization methods yield images consistent with the results, and statistical indicators are used to determine which method performs best at different frequencies.