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 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 hightemperature speckle images were developed to investigate the influences of noise on DIC method under high-temperature environment. Finally, the displacement fields of several special conditions were extracted from the simulated speckle images and experimental images, also the

Modal characterization of crack network development in an MgO containing refractory castable

“…These grainy regions are not present in Figure 13(a) to (d) because of filtering attributed to PCA. 17,18…”

“…They proposed the PCA as a tool to reduce dimensionality of the displacement data and mitigate noise. The effectiveness of PCA on noise reduction for DIC displacement fields was also evaluated by Hao et al 17 The authors used PCA to reduce spurious fluctuations in high-temperature DIC results. The procedure was applied to displacement fields for a synthetic set of images and another using images from a tensile test at high temperature.…”

DOE Microreactor Program: Summary of Experimental Capabilities Development and Activities

“…High temperatures will also result in haze effects from convection currents, which distort the image and introduce error to DIC measurements. Several countermeasures (Lyons 1996, Novak 2011, Leplay 2015, Su 2015, Ma 2019, Hao 2017, Bao 2019) will be investigated to improve the accuracy of high-temperature DIC against heat distortion.…”

On accounting for speckle extinction via DIC and PCA

“…Concerning DIC, if small displacements occur, it can be applied directly on images to extract kinematic bases for DIC analyses [8]. It can also be used for filtering out noise from measured displacement fields [9,10,11]. Other applications include estimating dynamic properties in viscoelastic materials [12], evaluating vibrations modes of cantilever plate [13], and clustering displacement vectors to analyze fracture mechanisms in rocks [14].…”