Fringe pattern normalization using conditional Generative Adversarial Networks

Abstract

Fringe pattern normalization is an important pre-processing operation in optical fringe analysis, and is usually required as the initial step for several phase retrieval methods. However, noise and rapid amplitude fluctuations in the fringe pattern pose significant challenges for fringe pattern normalization. In this paper, we propose a robust approach for fringe normalization using conditional Generative Adversarial Network, which works on the principle of image-to-image translation with conditioning on input images. The performance of the proposed method under different noise conditions and amplitude variations is demonstrated using numerical simulations. Further, the practical applicability of the proposed method is also tested with experimental data obtained in digital holographic interferometry.