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Stabilizing Defect Visibility Under Overexposure in Fringe-Based Imaging via γ Nonlinearity Analysis
Xiaolong Ma1, Xiaofei Wang1, Ruizhan Zhai1
1Qilu University of Technology (Shandong Academy of Sciences), Laser Institute, Shandong Academy of Scienses, China-Belarus Belt and Road Joint Laboratory on Intelligent Perception in Extreme Environments, Shandong Key Laboratory of Optoelectronic Sensing Technologies, National-Local joint Engineering Laboratory for Energy and Environment Fiber Smart Sensing Technologies, 3501 Daxue Road, Changqing District, Jinan 250353, China.
Gamma nonlinearity in fringe projection systems distorts image statistics, impacting defect detection. Correction stabilizes images by reducing variance, improving defect separability rather than just contrast.
Area of Science:
- Optics and Photonics
- Image Processing
- Metrology
Background:
- Phase-shifting fringe projection (PSFP) is crucial for 3D measurement and industrial inspection.
- Gamma (γ) nonlinearity in projector-camera systems is typically viewed as a phase error.
- This nonlinearity affects modulation stability, overexposure, and defect visibility in fringe imaging.
Purpose of the Study:
- To re-evaluate γ nonlinearity not as a phase error, but as a statistical distortion mechanism.
- To analyze the interaction between γ nonlinearity, fringe modulation, and frequency transfer.
- To clarify the role of γ correction in defect imaging under nonlinear and near-overexposure conditions.
Main Methods:
- Analytical modeling based on DC-AC decomposition of phase-shifting demodulation.
- Development of modulation-domain saliency formulations and frequency-domain harmonic energy ratio.
- Controlled experiments on highly reflective sheet-metal specimens.
Main Results:
- γ nonlinearity suppresses fringe fundamentals and introduces harmonics, compressing modulation contrast in bright regions.
- γ correction primarily offers statistical stabilization, not necessarily contrast amplification; it can decrease mean-contrast and SNR.
- Separability-based defect metrics consistently improve post-correction due to reduced nonlinear and saturation variance.
Conclusions:
- γ nonlinearity acts as a statistical distortion, reshaping image properties like modulation and defect saliency.
- The main benefit of γ correction is statistical stabilization, enhancing defect separability.
- Provides theoretical insight and practical guidance for robust defect imaging in challenging conditions.
