scholarly journals Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3563
Author(s):  
Zekun Jiao ◽  
Chibiao Ding ◽  
Longyong Chen ◽  
Fubo Zhang
Keyword(s):  
Bayesian Inference ◽  
3D Imaging ◽  
Three Dimensional ◽  
Super Resolution ◽  
Information Criterion ◽  
Radar Data ◽  
Imaging Method ◽  
Model Order Selection ◽  
Imaging Model ◽  
Imaging Performance

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.

scholarly journals Hyperspectral Three-Dimensional Fluorescence Imaging Using Snapshot Optical Tomography

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3652
Author(s):  
Cory Juntunen ◽  
Isabel M. Woller ◽  
Yongjin Sung
Keyword(s):  
Fourier Transform ◽  
3D Imaging ◽  
Optical Tomography ◽  
Three Dimensional ◽  
High Spectral Resolution ◽  
Functional Information ◽  
Fluorescent Beads ◽  
Different Depths ◽  
Projection Images ◽  
Imaging Performance

Hyperspectral three-dimensional (3D) imaging can provide both 3D structural and functional information of a specimen. The imaging throughput is typically very low due to the requirement of scanning mechanisms for different depths and wavelengths. Here we demonstrate hyperspectral 3D imaging using Snapshot projection optical tomography (SPOT) and Fourier-transform spectroscopy (FTS). SPOT allows us to instantaneously acquire the projection images corresponding to different viewing angles, while FTS allows us to perform hyperspectral imaging at high spectral resolution. Using fluorescent beads and sunflower pollens, we demonstrate the imaging performance of the developed system.

scholarly journals Strip-Mode Microwave Staring Correlated Imaging with Self-Calibration of Gain–Phase Errors

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1079 ◽  
Author(s):  
Rui Xia ◽  
Yuanyue Guo ◽  
Weidong Chen ◽  
Dongjin Wang
Keyword(s):  
Error Estimation ◽  
Phase Error ◽  
Super Resolution ◽  
Image Splicing ◽  
Initial Value ◽  
Self Calibration ◽  
Phase Errors ◽  
Imaging Model ◽  
Multiple Imaging ◽  
Imaging Performance

Microwave staring correlated imaging (MSCI) can realize super resolution imaging without the limit of relative motion with the target. However, gain–phase errors generally exist in the multi-transmitter array, which results in imaging model mismatch and degrades the imaging performance considerably. In order to solve the problem of MSCI with gain–phase error in a large scene, a method of MSCI with strip-mode self-calibration of gain–phase errors is proposed. The method divides the whole imaging scene into multiple imaging strips, then the strip target scattering coefficient and the gain–phase errors are combined into a multi-parameter optimization problem that can be solved by alternate iteration, and the error estimation results of the previous strip can be carried into the next strip as the initial value. All strips are processed in multiple rounds, and the gain–phase error estimation results of the last strip can be taken as the initial value and substituted into the first strip for the correlated processing of the next round. Finally, the whole imaging in a large scene can be achieved by multi-strip image splicing. Numerical simulations validate its potential advantages to shorten the imaging time dramatically and improve the imaging and gain–phase error estimation performance.

scholarly journals Compressive Sensing Based Three-Dimensional Imaging Method with Electro-Optic Modulation for Nonscanning Laser Radar

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 748
Author(s):  
Yulong An ◽  
Yanmei Zhang ◽  
Haichao Guo ◽  
Jing Wang
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Low Cost ◽  
Functional Model ◽  
Three Dimensional ◽  
Visible Spectrum ◽  
Depth Map ◽  
Imaging Method ◽  
Practical Applications ◽  
Electro Optic

Low-cost Laser Detection and Ranging (LiDAR) is crucial to three-dimensional (3D) imaging in applications such as remote sensing, target detection, and machine vision. In conventional nonscanning time-of-flight (TOF) LiDAR, the intensity map is obtained by a detector array and the depth map is measured in the time domain which requires costly sensors and short laser pulses. To overcome such limitations, this paper presents a nonscanning 3D laser imaging method that combines compressive sensing (CS) techniques and electro-optic modulation. In this novel scheme, electro-optic modulation is applied to map the range information into the intensity of echo pulses symmetrically and the measurements of pattern projection with symmetrical structure are received by the low bandwidth detector. The 3D imaging can be extracted from two gain modulated images that are recovered by solving underdetermined inverse problems. An integrated regularization model is proposed for the recovery problems and the minimization functional model is solved by a proposed algorithm applying the alternating direction method of multiplier (ADMM) technique. The simulation results on various subrates for 3D imaging indicate that our proposed method is feasible and achieves performance improvement over conventional methods in systems with hardware limitations. This novel method will be highly valuable for practical applications with advantages of low cost and flexible structure at wavelengths beyond visible spectrum.

scholarly journals A Review on Dual-Lens Fluorescence Microscopy for Three-Dimensional Imaging

2020 ◽  
Vol 8 ◽  
Author(s):  
Xiaoyan Li ◽  
Yubing Han ◽  
Wenjie Liu ◽  
Cuifang Kuang ◽  
Xu Liu ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
3D Imaging ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Super Resolution ◽  
Light Sheet ◽  
Animal Cells ◽  
3D Images ◽  
Single Lens ◽  
Model Animal

Three-dimensional (3D) imaging using dual-lens fluorescence microscopies is popular in observing fluorescently labeled biological samples, such as mammalian/model animal cells, tissues, and embryos. Specifically, dual-lens super-resolution fluorescence microscopy methods using two opposing objective lenses allow significantly higher axial resolution and better signal to noise ratio than traditional single-lens counterparts, and thus distinguish more details in 3D images of fine intracellular structures. For 3D imaging of thick tissues and entire embryos, dual-lens light-sheet fluorescence microscopy methods using two objective lenses, either orthogonal or non-orthogonal, to achieve selective plane illumination, can meet the requirements, and thus can be used to observe embryo development and structures of interest in thick tissues. This review summarizes both dual-lens fluorescence microscopy methods, including their principles, configurations, and 3D imaging applications, providing a guideline for biological laboratories with different 3D imaging needs.

scholarly journals A Microwave Three-Dimensional Imaging Method Based on Optimal Wave Spectrum Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7306
Author(s):  
Yan Zhang ◽  
Baoping Wang ◽  
Yang Fang ◽  
Zuxun Song
Keyword(s):  
3D Imaging ◽  
Imaging System ◽  
Wave Spectrum ◽  
Sampling Rate ◽  
Three Dimensional ◽  
Imaging Method ◽  
Data Sampling ◽  
Spectrum Reconstruction ◽  
Memory Space ◽  
Target Wave

Limited by the Shannon–Nyquist sampling law, the number of antenna elements and echo signal data of the traditional microwave three-dimensional (3D) imaging system are extremely high. Compressed sensing imaging methods based on sparse representation of target scene can reduce the data sampling rate, but the dictionary matrix of these methods takes a lot of memory, and the imaging has poor quality for continuously distributed targets. For the above problems, a microwave 3D imaging method based on optimal wave spectrum reconstruction and optimization with target reflectance gradient is proposed in this paper. Based on the analysis of the spatial distribution characteristics of the target echo in the frequency domain, this method constructs an orthogonal projection reconstruction model for the wavefront to realize the optimal reconstruction of the target wave spectrum. Then, the inverse Fourier transform of the optimal target wave spectrum is optimized according to the law of the target reflectance gradient distribution. The proposed method has the advantages of less memory space and less computation time. What is more, the method has a better imaging quality for the continuously distributed target. The computer simulation experiment and microwave anechoic chamber measurement experiment verify the effectiveness of the proposed method.

scholarly journals Knowledge-Aided Doppler Beam Sharpening Super-Resolution Imaging by Exploiting the Spatial Continuity Information

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1920 ◽  
Author(s):  
Hongmeng Chen ◽  
Zeyu Wang ◽  
Jing Liu ◽  
Xiaoli Yi ◽  
Hanwei Sun ◽  
...  
Keyword(s):  
Dwell Time ◽  
Spatial Coherence ◽  
Super Resolution ◽  
Data Set ◽  
Spatial Continuity ◽  
Range Resolution ◽  
Imaging Model ◽  
Equivalent Number ◽  
The Cross ◽  
Imaging Performance

This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm.

scholarly journals A Novel 3D Imaging Method for Airborne Downward-Looking Sparse Array SAR Based on Special Squint Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaozhen Ren ◽  
Yao Qin ◽  
Lihong Qiao
Keyword(s):  
3D Imaging ◽  
Three Dimensional ◽  
Imaging Method ◽  
Computationally Efficient ◽  
Theory Analysis ◽  
Imaging Geometry ◽  
Range Resolution ◽  
Sparse Array ◽  
Resolution Imaging ◽  
Cross Track

Three-dimensional (3D) imaging technology based on antenna array is one of the most important 3D synthetic aperture radar (SAR) high resolution imaging modes. In this paper, a novel 3D imaging method is proposed for airborne down-looking sparse array SAR based on the imaging geometry and the characteristic of echo signal. The key point of the proposed algorithm is the introduction of a special squint model in cross track processing to obtain accurate focusing. In this special squint model, point targets with different cross track positions have different squint angles at the same range resolution cell, which is different from the conventional squint SAR. However, after theory analysis and formulation deduction, the imaging procedure can be processed with the uniform reference function, and the phase compensation factors and algorithm realization procedure are demonstrated in detail. As the method requires only Fourier transform and multiplications and thus avoids interpolations, it is computationally efficient. Simulations with point scatterers are used to validate the method.

scholarly journals True-Color Three-Dimensional Imaging and Target Classification Based on Hyperspectral LiDAR

2019 ◽  
Vol 11 (13) ◽  
pp. 1541 ◽  
Author(s):  
Chen ◽  
Shi ◽  
Gong ◽  
Sun ◽  
Chen ◽  
...  
Keyword(s):  
3D Imaging ◽  
Spectral Band ◽  
Three Dimensional ◽  
Principal Component ◽  
Support Vector ◽  
Imaging Method ◽  
Spectral Correlation ◽  
Spectral Bands ◽  
True Color ◽  
Band Combinations

True-color three-dimensional (3D) imaging exploits spatial and spectral information and can enable accurate feature extraction and object classification. The existing methods, however, are limited by data collection mechanisms when realizing true-color 3D imaging. We overcome this problem and present a novel true-color 3D imaging method based on a 32-channel hyperspectral LiDAR (HSL) covering a 431–751 nm spectral range. We conducted two experiments, one with nine-color card papers and the other with seven different colored objects. We used the former to investigate the effect of true-color 3D imaging and determine the optimal spectral bands for compositing true-color, and the latter to explore the classification potential based on the true-color feature using polynomial support vector machine (SVM) and Gaussian naive Bayes (NB) classifiers. Since using all bands of HSL will cause color distortions, the optimal spectral band combination for better compositing the true-color were selected by principal component analysis (PCA) and spectral correlation measure (SCM); PCA emphasizes the amount of information in band combinations, while SCM focuses on correlation between bands. The results show that the true-color 3D imaging can be realized based on HSL measurements, and three spectral bands of 466, 546, and 626 nm were determined. Comparing reflectance of the three selected bands, the overall classification accuracy of seven different colored objects was improved by 14.6% and 8.25% based on SVM and NB, respectively, classifiers after converting spectral intensities into true-color information. Overall, this study demonstrated the potential of HSL system in retrieving true-color and facilitating target recognition, and can serve as a guide in developing future three-channel or multi-channel true-color LiDAR.

scholarly journals Taking Two-Photon Excitation (2PE) further: 2PE coupling to Far-field Optical Nanoscopy and Super Resolution Microscopy towards Three-dimensional (3D) Imaging of Thick Scattering Specimens

2013 ◽  
Vol 19 (S2) ◽  
pp. 166-167
Author(s):  
A. Diaspro ◽  
P. Bianchini ◽  
F. Cella Zanacchi ◽  
G. Vicidomini ◽  
B. Harke ◽  
...  
Keyword(s):  
3D Imaging ◽  
Three Dimensional ◽  
Super Resolution ◽  
Far Field ◽  
Two Photon ◽  
Photon Excitation ◽  
Super Resolution Microscopy ◽  
Two Photon Excitation

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

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