Differential signal-assisted method for adaptive analysis of fringe pattern

Area of Science:

• Optical Metrology
• Signal Processing
• Image Analysis

Background:

• Carrier signal recovery and phase retrieval are critical for single fringe image analysis in dynamic 3D measurements.
• Local mean decomposition, a common signal demodulation tool, suffers from mode-mixing, where noise and carrier signals can become indistinguishable.
• This ambiguity complicates the physical interpretation of decomposition results and hinders accurate phase retrieval.

Purpose of the Study:

• To address the mode-mixing problem in local mean decomposition for fringe pattern analysis.
• To develop an adaptive method for separating noise and carrier signals effectively.
• To improve the accuracy of phase retrieval in dynamic 3D measurements.

Main Methods:

• Designed a pair of differential signals based on noise characteristics to facilitate separation.
• Added these differential signals to the original signal for re-decomposition.
• Utilized the properties of same amplitude and opposite polarity of differential signals to isolate noise and carrier components.

Main Results:

• Successfully separated the differential signal and original noise from the carrier signal, minimizing negative impacts.
• Resolved the mode-mixing issue for high-frequency components.
• Enabled more accurate decomposition of low-frequency components, leading to improved fringe pattern analysis.

Conclusions:

• The proposed adaptive method effectively overcomes the mode-mixing problem in signal demodulation.
• It enhances the accuracy and reliability of phase retrieval in dynamic 3D measurements.
• The method demonstrates efficiency and applicability even for unstable signals.