- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Sparse Reconstruction-Based Thermal Imaging for Defect Detection
摘要： This paper proposes an idea of employing sparse reconstruction-based technique for thermal imaging defect detection. The implementation of the reconstruction technique is tested on a carbon fiber reinforced polymer test piece with artificially drilled defects and the test results are compared with the established cross correlation method. The two processes are compared in terms of defect detectability, their SNR variation with defect depth and their computation complexity. When compared with cross correlation algorithm, the technique is expected to solve memory space problems by compressing all information from large cross-correlated pulse video into a single reconstructed image as an output. Furthermore, in existing cross correlation methods, the pulse peak time shifts with defect depth. Hence, defect quantification algorithms, such as SNR calculation, require multiple frame analysis. Such algorithms are comparatively simplified in sparse reconstruction technique. This paper explores sparse reconstruction algorithm for resolving close-spaced defects. This paper further describes cross-validation method for optimization of a user parameter in sparse reconstruction method.
关键词： thermal nondestructive testing,sparse reconstruction,pulse compression,nondestructive evaluation and remote sensing,frequency modulated thermal wave imaging,Cross correlation algorithm
更新于2025-09-23 15:22:29
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[IEEE 2019 International Conference on Communication and Electronics Systems (ICCES) - Coimbatore, India (2019.7.17-2019.7.19)] 2019 International Conference on Communication and Electronics Systems (ICCES) - Synchronization of Distributed Photovoltaic Generation with an Active network using Phase Locked Loop technique
摘要： Optical tomographic imaging requires an accurate forward model as well as regularization to mitigate missing-data artifacts and to suppress noise. Nonlinear forward models can provide more accurate interpretation of the measured data than their linear counterparts, but they generally result in computationally prohibitive reconstruction algorithms. Although sparsity-driven regularizers significantly improve the quality of reconstructed image, they further increase the computational burden of imaging. In this paper, we present a novel iterative imaging method for optical tomography that combines a nonlinear forward model based on the beam propagation method (BPM) with an edge-preserving three-dimensional (3-D) total variation (TV) regularizer. The central element of our approach is a time-reversal scheme, which allows for an efficient computation of the derivative of the transmitted wave-field with respect to the distribution of the refractive index. This time-reversal scheme together with our stochastic proximal-gradient algorithm makes it possible to optimize under a nonlinear forward model in a computationally tractable way, thus enabling a high-quality imaging of the refractive index throughout the object. We demonstrate the effectiveness of our method through several experiments on simulated and experimentally measured data.
关键词： Optical phase tomography,sparse reconstruction,beam propagation method,total variation regularization,compressive sensing,stochastic proximal-gradient
更新于2025-09-23 15:21:01
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Ultrafast Light Source at 1.8 ??m Based on Thulium-Doped Fibers for Three-Photon Microscopy
摘要： Optical tomographic imaging requires an accurate forward model as well as regularization to mitigate missing-data artifacts and to suppress noise. Nonlinear forward models can provide more accurate interpretation of the measured data than their linear counterparts, but they generally result in computationally prohibitive reconstruction algorithms. Although sparsity-driven regularizers significantly improve the quality of reconstructed image, they further increase the computational burden of imaging. In this paper, we present a novel iterative imaging method for optical tomography that combines a nonlinear forward model based on the beam propagation method (BPM) with an edge-preserving three-dimensional (3-D) total variation (TV) regularizer. The central element of our approach is a time-reversal scheme, which allows for an efficient computation of the derivative of the transmitted wave-field with respect to the distribution of the refractive index. This time-reversal scheme together with our stochastic proximal-gradient algorithm makes it possible to optimize under a nonlinear forward model in a computationally tractable way, thus enabling a high-quality imaging of the refractive index throughout the object. We demonstrate the effectiveness of our method through several experiments on simulated and experimentally measured data.
关键词： Optical phase tomography,beam propagation method,compressive sensing,total variation regularization,stochastic proximal-gradient,sparse reconstruction
更新于2025-09-19 17:13:59
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Sparse Scene Recovery for High-Resolution Automobile FMCW SAR via Scaled Compressed Sensing
摘要： This paper introduces a sparse scene reconstruction algorithm for automobile frequency-modulated continuous-wave synthetic aperture radar (FMCW SAR) through scaled compressed sensing (CS). An FMCW radar leads to low manufacturing cost, compact realization, and low transmission power. An automobile SAR is more economical and easier to implement than typical SAR platforms (e.g., satellites and aircraft). We apply CS to randomly subsampled raw data of automobile FMCW SAR for sparse reconstruction. We exploit the fact that the velocity of an automobile is significantly lower than that of the SAR platforms, which leads to a vastly narrow bandwidth of an azimuth-matched filter in the azimuth compression of the range-Doppler algorithm (RDA). Low-frequency azimuth data have a fundamental effect on azimuth compression. We propose a new reconstruction scheme, scaled CS, which specializes in low-frequency information recovery for automobile SAR. The scheme is based on basis pursuit denoising (BPDN). A Ku-band FMCW SAR system is developed to verify the performance of the proposed algorithm. We mount our system on an automobile and collect FMCW SAR raw data in the stripmap mode with a van maintained a constant speed on a highway. The proposed reconstruction algorithm shows improved recovery performance for automobile FMCW SAR, which is validated by processing a high-resolution real SAR image.
关键词： Automobile synthetic aperture radar (SAR),frequency-modulated continuous-wave (FMCW) radar,compressed sensing (CS),SAR,sparse reconstruction.
更新于2025-09-11 14:15:04
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Joint Sparsity Constraint Interferometric ISAR Imaging for 3-D Geometry of Near-Field Targets with Sub-Apertures
摘要： This paper proposes a new interferometric near-field 3-D imaging approach based on multi-channel joint sparse reconstruction to solve the problems of conventional methods, i.e., the irrespective correlation of different channels in single-channel independent imaging which may lead to deviated positions of scattering points, and the low accuracy of imaging azimuth angle for real anisotropic targets. Firstly, two full-apertures are divided into several sub-apertures by the same standard; secondly, the joint sparse metric function is constructed based on scattering characteristics of the target in multi-channel status, and the improved Orthogonal Matching Pursuit (OMP) method is used for imaging solving, so as to obtain high-precision 3-D image of each sub-aperture; thirdly, comprehensive sub-aperture processing is performed using all sub-aperture 3-D images to obtain the final 3-D images; finally, validity of the proposed approach is verified by using simulation electromagnetic data and data measured in the anechoic chamber. Experimental results show that, compared with traditional interferometric ISAR imaging approaches, the algorithm proposed in this paper is able to provide a higher accuracy in scattering center reconstruction, and can effectively maintain relative phase information of channels.
关键词： interferometric inverse synthetic aperture radar,compressed sensing,joint sparse reconstruction,wide angle,near-field 3-D imaging
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Off-Grid Sparse Isar Imaging by Basis Shift Algorithm
摘要： In this paper a new and robust algorithm is proposed to achieve high resolution for inverse synthetic aperture radar (ISAR) imaging in the compressive sensing (CS) framework. Traditional CS based methods have to assume that unknown scatters exactly lie on the pre-divided grids, otherwise their reconstruction performance dropped significantly. So the basis shift algorithm is presented to provide performance improvement of the image reconstruction for off-grid target. The effectiveness and feasibility of the proposed method are investigated by the simulation results.
关键词： sparse reconstruction,off-grid,basis shift,ISAR imaging
更新于2025-09-10 09:29:36