Robust phase unwrapping in the presence of high noise remains an open issue. Especially, when both noise and fringe densities are high, pre-filtering may lead to phase dislocations and smoothing that complicate even more unwrapping. In this paper an approach to deal with high noise and to unwrap successfully phase data is proposed. Taking into account influence of noise in wrapped data, a calibration method of the 1st order spatial phase derivative is proposed and an iterative approach is presented. We demonstrate that the proposed method is able to process holographic phase data corrupted by non-Gaussian speckle decorrelation noise. The algorithm is validated by realistic numerical simulations in which the fringe density and noise standard deviation is progressively increased. Comparison with other established algorithms shows that the proposed algorithm exhibits better accuracy and shorter computation time, whereas others may fail to unwrap. The proposed algorithm is applied to phase data from digital holographic metrology and the unwrapped results demonstrate its practical effectiveness. The realistic simulations and experiments demonstrate that the proposed unwrapping algorithm is robust and fast in the presence of strong speckle decorrelation noise.

Speckle denoising can improve digital holographic interferometry phase measurements but may affect experimental accuracy. A deep-learning-based speckle denoising algorithm is developed using a conditional generative adversarial network. Two subnetworks, namely discriminator and generator networks, which refer to the U-Net and DenseNet layer structures are used to supervise network learning quality and denoising. Datasets obtained from speckle simulations are shown to provide improved noise feature extraction. The loss function is designed by considering the peak signal-to-noise ratio parameters to improve efficiency and accuracy. The proposed method thus shows better performance than other denoising algorithms for processing experimental strain data from digital holography.

A cement-based piezoelectric composite, modified by graphene oxide (GO), was prepared to study piezoresistive capacity. The testing confirms that GO is more effective than other carbon nanomaterials at improving piezoresistive sensitivity of cement-based composites, because the content of GO in cement paste was much lower than other carbon nanomaterials used in previously published research. Further investigation indicates that the addition of GO significantly improved the stability and repeatability for piezoresistive capacity of cement paste under cycle loads. Based on experiment results, the piezoresistive sensitivity of this composite depended on GO content, water-to-cement weight ratio (w/c) and water-loss rate, since the highest piezoresistive gauge factor value (GF = 35) was obtained when GO content was 0.05 wt.%, w/c was 0.35 and water-loss rate was 3%. Finally, microstructure analysis confirmed that conductivity and piezoresistivity were achieved through a tunneling effect and by contacting conduction that caused deformation of GO networks in the cement matrix.