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.

Two kinds of solid solution systems of Ta‐doped MgTiO3 were identified by X‐ray diffraction, which can be represented by the formulae MgTi1−x(Mg1/3Ta2/3)xO3 (0≤x<0.5) and MgTi1−xTaxO3 (0≤x<0.05). The conductivity and microwave dielectric loss for the two solid solution systems were examined by AC impedance and microwave resonator measurements, respectively. In the system MgTi1−x(Mg1/3Ta2/3)xO3, the mechanism for the solid solution formation is the isovalent substitution of for Ti4+. In the system MgTi1−xTaxO3, the doping mechanism is the aliovalent substitution of Ta5+ for Ti4+, where for a small amount Ta doping, the oxygen vacancies formed during the high‐temperature preparation are filled by an extra oxygen introduced from Ta2O5 and further Ta doping leads to an increase in the contents of and electrons, which was consistent with conductivity measurements. In both systems, the Q×f values improved, e.g., ∼17% for the isovalent substitution at x=0.08 and ∼10% for the aliovalent substitution at x=0.02. The filling oxygen vacancy and the substitution of Ta/Mg for Ti may contribute to the improvement of Q×f values for both systems.

In recent years, deep learning-based detection methods have been applied to pavement crack detection. In practical applications, surface cracks are divided into inner and edge regions for pavements with rough surfaces and complex environments. This creates difficulties in the image detection task. This paper is inspired by the U-Net semantic segmentation network and holistically nested edge detection network. A side-output part is added to the U-Net decoder that performs edge extraction and deep supervision. A network model combining two tasks that can output the semantic segmentation results of the crack image and the edge detection results of different scales is proposed. The model can be used for other tasks that need both semantic segmentation and edge detection. Finally, the segmentation and edge images are fused using different methods to improve the crack detection accuracy. The experimental results show that mean intersection over union reaches 69.32 on our dataset and 61.05 on another pavement dataset group that did not participate in training. Our model is better than other detection methods based on deep learning. The proposed method can increase the MIoU value by up to 5.55 and increase the MPA value by up to 10.41 when compared to previous semantic segmentation models.

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