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Atmospheric turbulence is a key factor contributing to data distortion in mid-to-long-range target observation tasks. Neural networks have become a powerful tool for dealing with such problems due to their strong ability to fit nonlinearities in the spatial domain. However, the degradation in data is not confined solely to the spatial domain but is also present in the frequency domain. In recent years, the academic community has come to recognize the significance of frequency domain information within neural networks. There remains a gap in research on how to combine dual-domain information to reconstruct high-quality images in the field of blind turbulence image restoration. Drawing upon the close association between spatial and frequency domain degradation information, we introduce a novel neural network architecture, termed Dual-Domain Removal Turbulence Network (DDRTNet), designed to improve the quality of reconstructed images. DDRTNet incorporates multiscale spatial and frequency domain attention mechanisms, combined with a dual-domain collaborative learning strategy, effectively integrating global and local information to achieve efficient restoration of atmospheric turbulence-degraded images. Experimental findings demonstrate significant advantages in performance for DDRTNet compared to existing methods, validating its effectiveness in the task of blind turbulence image restoration. [ABSTRACT FROM AUTHOR]

The real-time tracking of moving objects has extensive applications in various domains. Existing tracking methods typically utilize video image processing, but their performance is limited due to the high information throughput and computational requirements associated with processing continuous images. Additionally, imaging in certain spectral bands can be costly. This paper proposes a non-imaging real-time three-dimensional tracking technique for distant moving targets using single-pixel LiDAR. This novel approach involves compressing scene information from three-dimensional to one-dimensional space using spatial encoding modulation and then obtaining this information through single-pixel detection. A LiDAR system is constructed based on this method, where the peak position of the detected full-path one-dimensional echo signal is used to obtain the target distance, while the peak intensity is used to obtain the azimuth and pitch information of the moving target. The entire process requires minimal data collection and a low computational load, making it feasible for the real-time three-dimensional tracking of single or multiple moving targets. Outdoor experiments confirmed the efficacy of the proposed technology, achieving a distance accuracy of 0.45 m and an azimuth and pitch angle accuracy of approximately 0.03° in localizing and tracking a flying target at a distance of 3 km. [ABSTRACT FROM AUTHOR]

This article presents a miniaturized C-band triplexer fabricated with high-temperature superconducting (HTS) microstrip lines and to be applied to quantum computers. The triplexer consists of three bandpass filters and branch lines. In the design of the bandpass filters, folded step impedance resonators (SIRs) are firstly used to achieve good stopband performances by harmonic suppression. Secondly, cross-coupling structures are also utilized to further improve the passband edge steepness of the bandpass filters. Finally, the parasitic transmission zeros originating from the assembling box sizes are employed to achieve a deeper out-of-band rejection. Three bandpass filters are designed and connected to the common input port of the triplexer. After that, the triplexer is coarsely optimized by advanced design system (ADS) for time-saving and is finely optimized by Sonnet for better accuracy. The optimized layout of the triplexer is fabricated on a 2-inch diameter and 0.5 mm thickness MgO wafer; here, the MgO wafer is double-sided coated with HTS YBa2Cu3O7 (YBCO) thin films. The measured passband bandwidths (return loss greater than 12 dB) of the triplexer are 6.420-6.680 GHz, 6.780-7.030 GHz, and 7.110-7.380 GHz, respectively, which satisfy the required design specifications. [ABSTRACT FROM AUTHOR]

Fourier ptychography imaging technology is a method developed in recent years to achieve high-resolution imaging. In the traditional macro Fourier ptychography technology, the scanning method when the camera captures low-resolution images mostly uses the rectangular linear grid format. These acquired images contain a small amount of complementary information, and a large number of low-resolution images are needed to achieve high-resolution imaging. Redundant measurements will extend the sampling and reconstruction time, and require more computing resources. In this paper, we propose to obtain the target image spectral energy distribution by pre-sampling. And according to the energy distribution, we use irregular and non-uniform sampling modes to restore the target image. With the same number of samples and same reconstruction time, higher resolution imaging can be achieved compared with traditional methods. Simulation and experimental studies are carried out in this paper, and the results confirm the effectiveness of the proposed methods. Compared with the traditional sampling mode, the two sampling modes proposed in this paper increase the resolution from 4.49 lp/mm to 5.66 lp/mm and 5.04 lp/mm respectively. [ABSTRACT FROM AUTHOR]

Fourier ptychographic imaging technology is a new imaging method proposed in recent years. This technology captures multiple low-resolution images, and synthesizes them into a high-resolution image in the Fourier domain by a phase retrieval algorithm, breaking through the diffraction limit of the lens. In the field of macroscopic Fourier ptychographic imaging, most of the existing research generally focus on high-resolution imaging of static objects, and applying Fourier ptychographic imaging technology to dynamic objects is a hot research area now. At present, most of the researches are to use camera arrays combined with multiplexed lighting, deep learning or other algorithms, but the implementation of these methods is complicated or costly. Based on the diffraction theory of Fourier optics, this paper proposes that by expanding and focusing the illumination area, we can apply Fourier ptychographic imaging technology with a single camera to moving objects within a certain range. Theoretical analysis and experiments prove the feasibility of the proposed method. We successfully achieve high-resolution imaging of the dynamic object, increasing the resolution by about 2.5 times. This paper also researches the impact of speckles in the illuminated area on imaging results and proposes a processing method to reduce the impact of speckles.

Real-time tracking of fast-moving object have many important applications in various fields. However, it is a great challenge to track of fast-moving object with high frame rate in real-time by employing single-pixel imaging technique. In this paper, we present the first single-pixel imaging technique that measures zero-order and first-order geometric moments, which are leveraged to reconstruct and track the centroid of a fast-moving object in real time. This method requires only 3 geometric moment patterns to illuminate a moving object in one frame. And the corresponding intensities collected by a single-pixel detector are equivalent to the values of the zero-order and first-order geometric moments. We apply this new approach of measuring geometric moments to object tracking by detecting the centroid of the object in two experiments. The root mean squared errors in the transverse and axial directions are 5.46 pixels and 5.53 pixels respectively, according to the comparison of data captured by a camera system. In the second experiment, we successfully track a moving magnet with a frame rate up to 7400 Hz. The proposed scheme provides a new method for ultrafast target tracking applications.