The intensity distribution of fringe patterns becomes nonsinusoidal in real testing environments. Thus, the performance of existing phase shift extraction algorithms, which usually compute the desired phase shift by arccosine function or arcsine function, may be affected. In the presented paper, we report an arctangent-function-based technique to solve this disturbance. First, two orthogonal fringe patterns are constructed through subtraction and addition of two background-removed images. Second, the unequal amplitude between two new fringe patterns is eliminated using a normalization process. Third, the phase shift is determined by computing the norms of the two new images. The proposed method is fast and can be implemented easily in many applications. We verify the algorithm performance and robustness using both simulated and experimental data, indicating the high accuracy of the presented method.

Accurate detection of non-cooperative spacecraft based on optical sensor data is essential for critical space tasks, such as on-orbit servicing, rendezvous and docking, and debris removal. Traditional object detection methods struggle in the challenging space environment, which includes extreme variations in lighting, occlusions, differences in image scale. To address this problem, this paper proposes a high-precision, deep-learning-based, domain-adaptive detection method specifically tailored for non-cooperative spacecraft. The proposed algorithm focuses on two key elements: dataset creation and network structure design. First, we develop a spacecraft image generation algorithm using CycleGAN, facilitating seamless conversion between synthetic and real spacecraft images to bridge domain differences. Second, we combine a domain-adversarial neural network with YOLOv5 to create a robust detection model based on multi-scale domain adaptation. This approach enhances the YOLOv5 network’s ability to learn domain-invariant features from both synthetic and real spacecraft images. The effectiveness of our high-precision domain-adaptive detection method is verified through extensive experimentation. This method enables several novel and significant space applications, such as space rendezvous and docking and on-orbit servicing.

Phase unwrapping is a challenging task due to the presence of noise in the wrapped phase map. This paper describes, to the best of our knowledge, a novel technique, termed as noisy pixels identifying criteria, for phase unwrapping to minimize fake phase jump errors caused by noise and phase residues to a minimum. Its characteristic lies in fully leveraging the information contained in fringe patterns that can accurately identify noise-contaminated pixels of the wrapped phase map by constructing a phase-shifting version of the wrapped phase map. Based on it, a locally selective adaptive filtering strategy is introduced where noise filtering is applied exclusively to the identified noisy pixels, thereby avoiding the blurring and detail loss commonly caused by global noise filtering. We must note that the window size of our filter can be dynamically adjusted based on the number of non-noisy pixels in the local neighborhood, and is automatically expanded when insufficient valid pixels are present. Moreover, we developed an explicit filtering termination criterion that can ensure both stability and efficiency of the filtering process when iteratively applying the proposed method to the noisy wrapped phase map. Finally, with the aid of the proposed noisy pixels identifying criteria and iterative adaptive filtering, the denoised wrapped phase is unwrapped correctly by the transport of intensity equation (TIE) based phase unwrapping algorithm. Both simulation and experimental results confirm that the proposed method achieves superior phase reconstruction accuracy and robustness, even under complex and noisy measurement conditions.

Spatial structured light (SL) can achieve three-dimensional measurements with a single shot. As an important branch in the field of dynamic reconstruction, its accuracy, robustness, and density are of vital importance. Currently, there is a wide performance gap of spatial SL between dense reconstruction (but less accurate, e.g., speckle-based SL) and accurate reconstruction (but often sparser, e.g., shape-coded SL). The central problem lies in the coding strategy and the designed coding features. This paper aims to improve the density and quantity of reconstructed point clouds by spatial SL whilst also maintaining a high accuracy. Firstly, a new pseudo-2D pattern generation strategy was developed, which can improve the coding capacity of shape-coded SL greatly. Then, to extract the dense feature points robustly and accurately, an end-to-end corner detection method based on deep learning was developed. Finally, the pseudo-2D pattern was decoded with the aid of the epipolar constraint. Experimental results validated the effectiveness of the proposed system.

The introduction of a random phase-shifting technique into a shadow moiré system, where an equal and known (or unknown) phase step is used to demodulate the phase of interest, is beneficial for the improvement of measurement accuracy. However, in spite of recent advances in optical metrology phase-shifting techniques, simultaneously estimating unequal and unknown phase shifts from three random phase-shifting fringe patterns remains a significant challenge. This paper presents a one-dimensional minimizer-based technique to address this ill-posed problem of phase demodulation from random phase-shifting patterns. In this method, two new sets of connected fringe patterns, without background illumination, are constructed through normalizing the secondary fringe patterns. Then, a generalized phase-shifting algorithm is developed by utilizing the character of the modulation factor’s standard deviation distribution. Both numerical simulations and optical experiments are performed to demonstrate the high accuracy and robustness of the proposed method.