Hubing Du

To improve the efficiencies of traditional path-dependent phase unwrapping algorithms, we propose a graph-traversal-based path algorithm that enables fast and accurate phase unwrapping. The proposed method models the targeted wrapped phase map as a pixel-level undirected graph and can determine all the reliable seed points of the wrapped phase map with the help of an auxiliary wrapped map based on a gradient consistency measure. Then, many short unwrapping paths are constructed locally according to each seed point determined, enabling block-wise parallel unwrapping. This avoids redundant traversal and significantly improves computational efficiency. Finally, to fuse pixels unwrapped through distinct paths in some regions, a pixel-wise fusion strategy is employed to ensure phase continuity and global consistency. Numerical simulations and experimental validations demonstrate the accuracy of the proposed method. The results show that it performs comparably to the established algorithms, while improving computational speed by nearly an order of magnitude. Furthermore, the algorithm exhibits inherent scalability and robustness, providing an effective approach for the construction of unwrapping paths.

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.

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed 《中国光学》的前身是1985年创办的《中国光学与应用光学文摘》，当时是国内光学领域唯一的检索刊物。2008年底《中国光学与应用光学文摘》转型更名为《中国光学与应用光学》期刊，刊载内容突破了文摘的限制，正式成为学术刊物。

In this work, we propose a hyper ellipse fitting-based high-precision random two-frame phase shifting algorithm to improve the accuracy of phase retrieval. This method includes a process of Gram-Schmidt orthonormalization, followed by a hyper ellipse fitting procedure. The Gram-Schmidt orthonormalization algorithm constructs a quadrature fringe pattern relative to the original fringe pattern. These two quadrature fringe patterns are then fed into the hyper ellipse fitting procedure, which reconstructs the phase map and refines the background light to produce the final accurate phase of interest. Due to the hyper ellipse fitting procedure, the demodulation results are significantly improved in many cases. This method allows us to design a two-shot phase reconstruction algorithm without the need for least squares iteration or pre-filtering, effectively mitigating residual background to the greatest extent. It combines the advantages of both the Gram-Schmidt orthonormalization method and the Lissajous ellipse fitting method, making our hyper ellipse fitting approach a simple, flexible, and accurate phase retrieval algorithm. Experiments show that by using the weighted least squares method and adjusting the weights, this method can prioritize data points with more significant information or higher reliability, ensuring more accurate estimation of the ellipse parameters.

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.