Differential signal-assisted method for adaptive analysis of fringe pattern.

In dynamic 3D measurements, the recovering carrier signal as well as phase retrieval are important issues for single fringe image analysis. Local mean decomposition is a powerful signal demodulation tool, but it usually encounters an obstacle, namely, the mode-mixing problem. Then different components, especially noise and carrier signals, are all probably mixed in one of the decomposition results, thus confusing its physical meaning. Utilizing the characteristics of original noise, we design a pair of differential signals based on the conditions that two mixed components should meet to be separated completely and then add them to the original signal. Re-decomposing the newly formed signal, the differential signal, along with the original noise, will be separated from the carrier signal, leaving very little negative impact due to the characteristics of the same amplitude and opposite polarity of the differential signal. With the mode-mixing problem of high-frequency components being resolved, the decomposition of the following low-frequency components becomes more reasonable, facilitating the fringe pattern analysis and further phase retrieval. The proposed method is suitably used for the signal, even though it is not stable. Experiments illustrate the efficiency of this novel adaptive method.