Normalization of fringe patterns using the bidimensional empirical mode decomposition and the Hilbert transform

Abstract

We evaluate a data-driven technique to perform bias suppression and modulation normalization of fringe patterns. The proposed technique uses a bidimensional empirical mode decomposition method to decompose a fringe pattern in a set of intrinsic frequency modes and the partial Hilbert transform to characterize the local amplitude of the modes in order to perform the normalization. The performance of the technique is tested using computer simulated fringe patterns of different fringe densities and illumination defects with high local variations of the modulation, and its advantages and limitations are discussed. Finally, the performance of the normalization approach in processing real data is also illustrated.

Theory and algorithms of an efficient fringe analysis technology for automatic measurement applications

“…For a given fringe pattern, the procedure of background light suppression and modulation light normalization is denominated fringe-pattern normalization [25,26,28,29]. This process gives as a result a pure sinusoidal phase modulated signal.…”

Hilbert-Huang processing and analysis of complex fringe patterns

“…Some comparison of different interpolation techniques is given in [49]. Reported applications of BEMD in the fringe pattern processing and analysis deal with noise reduction in digital speckle interferometry [50], fringe pattern normalization [51], phase measurement in temporal speckle interferometry [52], evaluation of amplitude encoded fringe patterns [53] and, most recently, background and noise removal from fringe patterns [54][55][56]. The practical impact of BEMD is limited by the calculation time lengthened by very costly 2D surface spline interpolation on the irregular extrema grid.…”

“…Several normalization solutions have been proposed over the last decade. They include interferogram clipping [32,33], 2-D envelope transform coupled with spin filtering [73], two orthogonal bandpass filtering [74], the quadrature transform isotropic operator (Hilbert transform) algorithm [75], adaptive filtering as a linear combination of isotropic bandpass filtering [76], continuous wavelet transform (CWT) processing coupled with synthesized interferogram clipping [77], directional derivative approach [78], and the bidimensional empirical mode decomposition aided by the partial Hilbert transformation [51].…”

Optical Interference Signal Processing in Precision Dimension Measurement

“…The image processing system often get data from the CMOS or CCD cameras, and use digital image processing methods to calculate the phase shift. The image processing technique offers many new solutions for the fringe analysis (Patorski & Styk, 2006, Marengo-Rodriguez et al, 2007, Bernini et al, 2009, Larkin et al, 2001, Pokorski & Patorski, 2010, Zhang, 2011 and extends the application fields of fringe analysis (Geng, 2011, Riphagen et al, 2008.…”