Analysis and comparison of limit detection capabilities of three active synthetic aperture imaging techniques

Lei, 董磊 Dong; Zhen-wu, 卢振武 Lu; Xin-yue, 刘欣悦 Liu

doi:10.3788/co.20191201.0138

Cited by 4 publications

(3 citation statements)

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“…In this subsection, the amplitude product and phase difference information is demodulated from echo signal (4). Assuming that the number of columns and rows of the detector array after sparse is N x × N y , where N y is even, the detector coordinate…”

Section: Signal Demodulationmentioning

confidence: 99%

“…There are generally three types of unconventional ground-based imaging techniques for space targets: Fourier telescopy, 1 intensity correlation imaging, 2 and sheared-beam imaging (SBI; also called sheared coherent interferometric photography), 3 – 5 which are high-resolution computational imaging techniques 6 that use laser illumination and do not need an optical imaging system. One of these, SBI technology, has the advantage of quasi-real-time imaging, which is particularly suited to the observation of fast-moving targets in long-distance through atmospheric turbulence.…”

Section: Introductionmentioning

confidence: 99%

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Four-beam sparse phase retrieval algorithm for sheared-beam imaging

Chen

Zhang

et al. 2023

Opt. Eng.

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.Sheared-beam imaging (SBI) is an effective way of imaging through turbulent medium, such as atmosphere or scattering liquid. Traditionally, the imaging is based on the laser transmitter array consisting of three beams or five beams for coherent illumination to the remote object. Compared with the existing SBI methods, the four-beam sparse sampling imaging method has been proposed, which may have more advantages; it not only sparses the detector elements but also reduces the number of emitted beams. However, the traditional phase retrieval algorithms are not suitable for the four-beam sparse sampling imaging. We propose a four-beam sparse phase retrieval (F-BSPR) algorithm, which uses the phase differences from both horizontal and vertical components and the phase differences from other components when the phase is retrieving. The proposed phase retrieval algorithm can better connect the phase difference and improve the accuracy of the phase retrieval. Furthermore, the imaging quality is improved. Simulation and experimental results show that the proposed algorithm is effective and feasible when the number of detector elements is sparse by 50%. Compared to the traditional four-beam phase retrieval method, the proposed F-BSPR method has better imaging quality and robustness.

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Section: Signal Demodulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Four-beam sparse phase retrieval algorithm for sheared-beam imaging

Chen

Zhang

et al. 2023

Opt. Eng.

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“…However, this method is not suitable for the 3D reconstruction of large outdoor scenes. Complex surface objects are blocked by surrounding objects, and there are still some difcult problems to be solved, such as the blocked part being difcult to convert from two-dimensional images to 3D information [8,9].…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction and Measurement Analysis of a Dense Point Cloud Fused with a Depth Image

Qiao,

Lv,

Zhang

2023

International Journal of Optics

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To solve the problems of holes, noise, and texture information missing in the traditional incremental reconstruction of complex surface objects, a 3D reconstruction method of depth image fusion surface dense point clouds is proposed, and texture feature creation is combined to obtain a 3D reconstruction model that takes into account the main body and details of the reconstructed object. First, the mechanism of surface dense reconstruction based on the patch-based multiview stereo (PMVS) algorithm is analyzed. Combined with the principle of view angle selection of stereo images, surface point cloud density reconstruction is performed. Then, the depth value is optimized by the region growing method, and the optimization model is established. The depth image is fused into a dense surface, and the reconstructed part is supplemented by the depth information. Finally, the Markov random field (MRF) is introduced to describe the richness of image details, and combined with the calculating method of the area coordinate, the texture coordinates are accurately calculated to reproduce the texture details of the 3D reconstruction model. 3D reconstruction experiments are performed on multiple indoor and outdoor model surfaces, and the experimental results show that the proposed method can achieve complete and accurate reconstruction of complex surface objects. Our method provides technical support for complex surface topography detection and has industrial practical significance.

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Spatial domain sparse reconstruction algorithm of sheared beam imaging

Chen¹,

Liu²,

Zhang³

et al. 2022

Acta Phys. Sin.

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Sheared beam imaging (SBI) is considered a computational imaging technique that transmitted three sheared coherent laser beamlets for illumination, and a sensor array to receive the intensity of speckle pattern reflected from the target. SBI can be used to image remote objects through a turbulent medium without the need for any adaptive optics. However, while imaging low-orbit moving targets, the number of detectors of sensor array required for the receiving system of SBI are very large, and the development of sensor array is difficult and costly. This paper proposes the spatial domain sparse sampling technique for the SBI system through transmitting five laser beamlets to illuminate the target carrying more of its spectral information, which can reduce the number of detectors of the sensor array. Firstly, the principle of the sparse imaging technique is deduced. Then, a sparse reconstruction algorithm is studied. The phase difference and amplitude information of the target in the echo signal after sparse sampling can be extracted accurately by searching for the accurate positions of the beat frequency components. The wavefront phases can be demodulated by the least-squares method, and wavefront amplitude can be obtained by the algebraic operation of speckle amplitude. The reconstructed wavefront is used to formulate the two-dimension image of the target. Theoretically, without affecting the resolution, the number of detectors of the sensor array can be reduced to half of the traditional three-beam method, which breaks through the limitation that the detector spacing of sensor array is equal to the shear lengths of beamlet. From simulation results, when the number of detectors of the sensor array is reduced by fifty percent, the proposed sparse reconstruction algorithm has almost the same quality as the reconstructed image with the traditional three-beam method.

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Analysis and comparison of limit detection capabilities of three active synthetic aperture imaging techniques

Cited by 4 publications

References 0 publications

Four-beam sparse phase retrieval algorithm for sheared-beam imaging

Four-beam sparse phase retrieval algorithm for sheared-beam imaging

3D Reconstruction and Measurement Analysis of a Dense Point Cloud Fused with a Depth Image

Spatial domain sparse reconstruction algorithm of sheared beam imaging

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