Fringe pattern denoising using averaging based on nonlocal self-similarity

Digital image processing for fringe patterns is an important procedure in optical interferometry. Filtering off noise from fringe patterns is one of the key tasks for extraction of the phase field. Spin filters proposed by Yu et al. [Appl. Opt. 33(1994), 41(2002), et al.] have been proven to be effective denoising methods. In this paper, we develop a nonlocal self-similarity filter, which averages similar pixels searched for in whole image instead of in a local fringe direction as the spin filters do. Although simple and free of the fringe orientation estimation, involving more pixels with higher similarity levels, our algorithm has stronger robustness against noise and thus denoises fringe patterns more effectively. Simulation and experimental results show that our algorithm outperforms related filters both in preserving smooth fringes and in reducing blurring effects and quantitative errors.