Fringe projection profilometry (FPP) and digital image correlation (DIC) are widely applied in three-dimensional (3D) measurements. The combination of DIC and FPP can effectively overcome their respective shortcomings. However, the speckle on the surface of an object seriously affects the quality and modulation of fringe images captured by cameras, which will lead to non-negligible errors in the measurement results. In this paper, we propose a fringe image extraction method based on deep learning technology, which transforms speckle-embedded fringe images into speckle-free fringe images. The principle of the proposed method, 3D coordinate calculation, and deformation measurements are introduced. Compared with the traditional 3D-DIC method, the experimental results show that this method is effective and precise.

In this paper, a high-precision and flexible intrinsic and extrinsic parameters calibration method for non-overlapping array cameras is proposed. In the camera array system, each camera employs an individual planar calibration board to achieve intrinsic parameters calibration. Without assistance of any other coded or un-coded target, the extrinsic parameters of array cameras can be flexibly calibrated based on close-range photogrammetry. To overcome the disadvantages of low precision under a large field of view in close-range photogrammetry, a refinement coordinates mapping (RCM) method to achieve high-precision reconstruction of targets is developed. The principles of single-camera calibration, close-range photogrammetry, and the extrinsic calibration method are introduced, respectively. Experiments are performed to analyze the stability of single-camera calibration and the world coordinates reconstruction of targets. The results of the re-projection error in extrinsic parameters calibration show that the developed RCM method has high precision.

Here, we fabricated a home-made strain loading device and proposed a compressive strain measurement method, by which the strain-dependent optical absorption properties of MoS₂ monolayers under compression has been studied.

Underwater images typically suffer from severe color distortion, low contrast, and poor visibility due to light absorption and scattering in aquatic environments. To address these challenges, we propose a novel Dual-Attention GAN (DA-GAN), specifically designed to enhance contrast and correct color distortions in underwater images. DA-GAN features three key innovations: a Hybrid CNN-Attention Bottleneck Layer (HCABL) for efficient enhancement of local and global features, a Transformer-based Channel Attention Module (TCAM) for color distortion removal, and a Multi-Color Space Loss function to improve contrast and visibility. HCABL employs parallel attention mechanisms and depth-wise separable convolutions to enhance both global context and local spatial details. TCAM enhances the model’s sensitivity to various color channels, enabling effective correction of complex color distortions. Furthermore, the Multi-Color Space Loss function, tailored for the HSV color space and the image’s bright and dark channels, further enhance contrast and visibility. Comprehensive ablation studies validate the effectiveness of each component, while extensive comparison experiments demonstrate that DA-GAN achieves state-of-the-art or comparable performance across six public underwater image datasets (SUIM, UIEB, EUVP, LSUI, UCCS and SQUID). The source code is publicly available at https: //github.com/littlebearRedgood/DAGAN.

In fringe projection profilometry, high-order harmonics information of distorted fringe will lead to errors in the phase estimation. In order to solve this problem, a point-wise phase estimation method based on a neural network (PWPE-NN) is proposed in this paper. The complex nonlinear mapping relationship between the gray values and the phase under non-sinusoidal distortion is constructed by using the simple neural network model. It establishes a novel implicit expression for phase solution without complicated measurement operations. Compared with the previous method of combining local image information, it can accurately calculate each phase value by point. The comparison results show that the traditional method is with periodic phase errors, while the proposed method can effectively eliminate phase errors caused by non-sinusoidal phase shifting.

A flexible, easy-to-implement, one-step calibration method of the light-sectioning system is proposed for surface topography using a mirror adapter. The calibration method employs an arbitrary non-planar object with feature points as a target. With the assistance of the mirror adapter, the real and the reflected images of calibration are spatially separated and captured by a camera. It is developed as a direct calculation (DC) method and a virtual camera (VC) method to optimize the full parameters of the system. The DC method estimates the relative rotation and translation, respectively, and the VC method regards the two images as captured by two cameras in a physical sense through image flipping. The parameters of the laser plane can be solved accordingly. A numerical simulation is performed to analyze the precision of these two methods. A three-dimensional (3D) toy and a mechanical element are measured to verify the validity in translation experiments.