An adaptive approach is presented for noise reduction of optical fringe patterns using multivariate empirical mode decomposition. Adjacent rows and columns of patterns are treated as multichannel signals and are decomposed into multiscale components. Fringe patterns are reconstructed with less noise by simply thresholding coefficients in different scales. The proposed approach can better concentrate local main components of fringe signals into single scale, compared with the conventional multiscale denoising method. A simulated pattern and an actual example are examined. Signal-to-noise ratio (SNR) of the simulated pattern is more than doubled.

A triple-frequency color fringe-projected technique is presented to measure dynamic objects. Three fringe patterns with a carrier frequency ratio of 1:3:9 are encoded in red, green, and blue channels of a color fringe pattern and projected onto an object's surface. Bidimensional empirical mode decomposition is used for decoupling the cross talk among color channels and for extracting the fundamental frequency components of the three fringe patterns. The unwrapped phase distribution of the high-frequency fringe is retrieved by a three-step phase unwrapping strategy to recover the object's height distribution. Owing to its use of only a single snapshot, the technique is suitable for measuring dynamically changing objects with large discontinuity or spatially isolated surfaces.

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