Displacement field denoising for high-temperature digital image correlation using principal component analysis

ABSTRACT

Principal component analysis (PCA) was extended to minimize the noise effect in digital image correlation (DIC) measurement under a high-temperature atmosphere environment. First, the principle of PCA was introduced, and the singular vectors and singular values for each component of the displacement fields from DIC were obtained. Then, the simulated high-temperature speckle images were developed to investigate the influences of noise on the DIC method under a high-temperature environment. Finally, the displacement fields of several special conditions were extracted from the simulated speckle images and experimental images; the effects of noise on the PCA were also analyzed.

KEYWORDS:

• High-temperature
• digital image correlation
• principal component analysis
• thermal disturbance
• noise reduction
• singular value decomposition

Funding

The authors are grateful for the financial support provided by the National Natural Science Foundation of China (Grant Nos. 11502260, 11232008, 11372118).