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.

With the rapid development of deep learning, pavement crack detection has started to shift from traditional manual visual inspection to automated detection; however, automatic detection is still a challenge due to many complex interference conditions on pavements. To solve the problem of shadow interference in pavement crack detection, this paper proposes an improved shadow generation network, named Texture Self-Supervised CycleGAN (CycleGAN-TSS), which can improve the effect of generation and can be used to augment the band of shadowed images of pavement cracks. We selected various images from three public datasets, namely Crack500, cracktree200, and CFD, to create shadowed pavement-crack images and fed them into CycleGAN-TSS for training to inspect the generation effect of the network. To verify the effect of the proposed method on crack segmentation with shadow interference, the segmentation results of the augmented dataset were compared with those of the original dataset, using the U-Net. The results show that the segmentation network achieved a higher crack recognition accuracy after the augmented dataset was used for training. Our method, which involves generating shadowed images to augment the dataset and putting them into the training network, can effectively improve the anti-shadow interference ability of the crack segmentation network. The research in this paper also provides a feasible method for improving detection accuracy under other interference conditions in future pavement recognition work.

Weld stability is directly related to the safety and reliability of engineering, and continuous improvement of its detection technology has great research significance. This paper presents a novel method for weld stability detection based on color digital holography. A color digital holography optical path was designed to capture the holograms of welds under varying loads. Several common denoising algorithms were used to deal with speckle noise in the wrapped phase, among which the 4-f optical simulation integrated cycle-consistent generative adversarial network (4f-CycleGAN) denoising algorithm based on deep learning was more suitable for the color digital holographic detection system. The three-dimensional deformation fields of three samples (lap-jointed, butt-jointed, and defective butt-jointed aluminum alloy plates) under different loads were calculated. The center profile of the deformation field in the direction of load and holographic reconstruction images are used to determine the position of the weld. The coefficient of variation near the weld was used to evaluate the weld stability. The coefficient of variation for lap-jointed, butt-jointed and defective butt-jointed plates are 0.0335 (<0.36, good stability), 0.1240 (0.36, poor stability), respectively. The research results of this paper provide a new strategy for detecting weld stability.

Phase unwrapping is essential for many phase-centric optical imaging and metrology techniques, yet remains a challenge under high speckle noise. A deep learning-based speckle phase unwrapping algorithm was developed on the basis of a physics-informed convolutional-Fourier neural network, relying on an uncertainty-aware mathematical model of speckle. To address the limitation that traditional spatial-domain CNN methods suffer from inadequacy in extracting frequency-domain interference features during phase unwrapping under speckle noise, we integrated the speckle mathematical model into the network via a parallel convolution-Fourier architecture and employed an integrated loss function with speckle noise, phase gradient, and absolute phase to significantly enhance the unwrapping performance of the network. Experimental results demonstrate that the proposed method achieves superior performance over other phase unwrapping algorithms in both simulations and digital holography experiments. By unifying spatial-domain and frequency-domain feature extraction via a physics-informed deep learning architecture, this method exhibits great potential for extension to broader information processing tasks.