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Conference on Holography, Diffractive Optics, and Applications XI

作者： Zhang, Qin Jian, Mengjing Liu, Chao Wan, Yuhong Beijing Univ Technol Coll Phys & Optoelect Fac Sci 100 Ping Le Yuan Beijing 100124 Peoples R China

ISBN: (数字)9781510646469

ISBN: (纸本)9781510646469;9781510646452

Fresnel incoherent correlation holography(FINCH) is a well-established incoherent imaging technique. The holograms of incoherent objects can be recorded and three-dimensional information of the original object can be retrieved by FINCH. The technique can be used as single molecule three-dimensional localization method owe to the self-interference hologram coded 3D spatial information of the object. Localization algorithm based on reconstructed image is usually employed to extract 3D spatial information of the object. Reconstruction distance has a great impact on reconstructed image quality, thus affect the localization accuracy. In order to improve the localization accuracy and the reconstructed image quality, autofocus algorithm is introduced to help finding accurate reconstruction distance in the Fresnel Incoherent Correlation Holographic localization microscopic imaging. The performances of autofocus algorithm under different evaluation parameters of reconstruction images are investigated, and the suitable autofocus algorithm is used during hologram reconstruction. The localization algorithm is utilized based on the reconstructed image. The localization accuracy and imaging quality are compared with and without employing the autofocus algorithm. The enhanced localization accuracy is realized in the actual FINCH localization microscopic imaging system.

关键词： Digital holography Incoherent holography reconstruction distance autofocus algorithm localization accuracy

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A back-projection autofocus algorithm based on flight trajectory optimization for synthetic aperture radar imaging

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2016年第2期27卷 411-431页

作者： Saeedi, Jamal Faez, Karim Amirkabir Univ Technol Tehran Polytech Dept Elect Engn 424 Hafez Ave Tehran Iran

In this paper, a new autofocus algorithm is presented for back-projection (BP) image formation of synthetic aperture radar (SAR) imaging. The approach is based on maximizing a cost function obtained by prominent points in different sub-apertures of constructed SAR image by varying the flight trajectory parameters. While image-quality-based autofocus approach together with BP algorithm can be computationally intensive, we use approximations that allow optimal corrections to be derived. The approach is applicable for focusing different signal processing algorithms by obtaining modified flight trajectory parameters. Different examples demonstrate the effectiveness of the new autofocus approach applied to the frequency modulated continuous wave mode SAR dataset.

关键词： Synthetic aperture radar autofocus algorithm Back-projection Frequency modulated continuous wave Strip-map

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autofocus algorithm using blind homomorphic deconvolution for synthetic aperture radar imaging

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IET RADAR SONAR AND NAVIGATION 2015年第7期9卷 900-906页

作者： Shao, Peng Xing, Mengdao Xia, Xiang-Gen Li, Yachao Li, Xueshi Bao, Zheng Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

In airborne case, synthetic aperture radar images generally suffer from the deterioration because of the unknown phase error caused by unstable platform and atmosphere perturbation. To obtain the phase error, a novel autofocus algorithm referred to as blind homomorphic deconvolution autofocus algorithm is proposed in this study. In this method, a wavelet scaling function is used to construct a smooth subspace that is orthogonal to noise subspace. Then, the phase error can be separated and reconstructed by the generated smooth subspace based on the differences of the smoothness properties between phase error and image reflectivity. Compared with the traditional autofocus methods, the proposed method does not require an iterative estimation. Thus, the computational complexity can be significantly reduced. Simulation and real data processing results validate the effectiveness of the proposed method.

关键词： synthetic aperture radar radar imaging deconvolution airborne radar phase estimation iterative methods wavelet transforms image reconstruction computational complexity autofocus algorithm blind homomorphic deconvolution synthetic aperture radar imaging unknown phase error atmosphere perturbation wavelet scaling function iterative estimation computational complexity data processing

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A novel BP-GA based autofocus method for detection of circuit board components

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OPTICS COMMUNICATIONS 2025年 575卷

作者： Zhu, Guangyi Wang, Siyuan Wang, Lilin Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China

Optical micro-inspection systems use different focusing methods depending on the inspection requirements of different scenarios. In practical industrial micro-inspection, grid samples have a wide variety of components, for example, electronic components on circuit boards and transistors on electronic chips. Changes in the surrounding environment (e.g., brightness of light, flatness of the platform, and temperature, etc.) during the inspection of these processed parts may lead to out-of-focus of the object under the microscope. Therefore, this paper proposes an autofocus algorithm to cope with the complex environment during inspection. The algorithm is based on feature vectors reflecting the external geometry of the spot and the internal energy distribution, and is combined with a back-propagation neural network with a genetic algorithm (GA) to enhance the focusing capability of the optical microscope. Preliminary numerical test results show that because of the bias problem in the focusing system, the accuracy of the neural network in calculating the defocused amount (DA) is significantly improved, despite the pitfalls of its generalization ability and the possibility of endless loops during the focusing process. In order to further solve the pitfalls of neural networks, this paper introduces a full reference image evaluation model into the optical microscope system and finally develops the autofocus software. Focusing tests using the developed software for the inspection of real components demonstrate that the introduced full reference image evaluation model not only expands the focusing distance of the inspection system, but also prevents the autofocus algorithm from falling into a dead loop.

关键词： Optical microscope autofocus algorithm Machine learning Full reference image evaluation model Component inspection

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Flexible and universal autofocus based on amplitude difference of fractional Fourier transform

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OPTICS AND LASERS IN ENGINEERING 2024年 175卷

作者： Li, Ziyang Zhou, Xuyang Wang, Yiran Huang, Guancheng Liu, Shutian Gao, Bin Liu, Zhengjun Harbin Inst Technol Sch Phys Harbin 150001 Peoples R China Heilongjiang Univ Coll Data Sci & Technol Harbin 150080 Peoples R China

autofocus technology is a crucial technique in computational optical imaging. The clarity evaluation function (CEF) serves as the criterion for autofocus, making it the core of the autofocus algorithm. In this paper, we propose a CEF based on amplitude differences of fractional Fourier transform (ADFrFT). ADFrFT extracts information from the fractional domain, which corresponds to the fusion of the spatial and frequency domains. This renders ADFrFT highly accurate and robust to noise, making it suitable for various types of sample images. Additionally, ADFrFT provides an added dimension of flexibility to address different autofocus requirements for distinct sample image types and focusing scenarios. Simulations and experiments confirmed the effectiveness of ADFrFT, and we improved the imaging resolution via distance calibration in the coaxial multi-distance coherent diffraction imaging (MDCDI) experiment.

关键词： Coherent diffraction imaging autofocus algorithm Clarity evaluation function Phase retrieval

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Impact Analysis and Compensation Methods of Frequency Synchronization Errors in Distributed Geosynchronous Synthetic Aperture Radar

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REMOTE SENSING 2024年第8期16卷 1470-1470页

作者： Sun, Xiaoying Chen, Leping Zhou, Zhengquan Du, Huagui Huang, Xiaotao Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms.

关键词： distributed GEO SAR frequency synchronization oscillator errors compensation phase autofocus algorithm

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FD-ISAR Translational Compensation algorithm Based on Observation Subset

FD-ISAR Translational Compensation Algorithm Based on Observ...

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作者： Wenying Lian Kefei Liao Xinghua Liu School of Information and Communication Guilin University of Electronic Technology

Frequency Diverse Inverse Synthetic Aperture Radar(FD-ISAR) transmits signals of different frequencies at different observation times to synthesize large broadband signals, which can be reduced transmitter costs. However, when FD-ISAR transmits single-frequency signals with different arrangements, it will cause the inability to obtain the range profile at a single observation time and the failure of the Fourier transforms imaging method, which will affect FDISAR translational compensation. To solve this problem, this paper proposes a FD-ISAR translational compensation algorithm that flexibly sets the frequency offset of the transmitted signal for non-cooperative targets. The algorithm uses the minimum image entropy criterion as the objective function, cross-correlation algorithm rough estimation and back projection(Back Projection, BP) autofocus algorithm jointly realizes motion parameter estimation and translation compensation of non-cooperative targets. The simulation experiment tests the effectiveness of this method for the translational compensation of non-cooperative targets with unknown moving speed.

关键词： Inverse synthetic aperture radar Translation compensation Back-projection autofocus algorithm Minimum entropy

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autofocus approach for sparse aperture inverse synthetic aperture radar imaging

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ELECTRONICS LETTERS 2015年第22期51卷 1811-1812页

作者： Xiao, Da Su, Fulin Gao, Jianjun Harbin Inst Technol Dept Informat Engn Harbin 150001 Peoples R China

In the inverse synthetic aperture radar imaging, the autofocus is a required step for generating high-quality images. However, due to many factors, the aperture data may be sparse so that the classical motion compensation approaches and imaging algorithms are not proper. On the basis of the compressed sensing technique and two optimisation methods (the gradient projection method and the conjugate gradient method), a novel autofocus algorithm, which can be used in sparse aperture imaging, is proposed in this Letter. The phase errors induced by the translational Doppler frequency are estimated and the focused image is reconstructed simultaneously by dual iterative computation. This approach is verified by real data processing.

关键词： Doppler radar compressed sensing gradient methods iterative methods radar imaging synthetic aperture radar Doppler frequency aperture data autofocus algorithm autofocus approach compressed sensing technique conjugate gradient method gradient projection method imaging algorithms iterative computation motion compensation approaches optimisation methods phase errors real data processing sparse aperture inverse synthetic aperture radar imaging

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ISAR autofocus Based on Image Entropy Optimization algorithm

ISAR Autofocus Based on Image Entropy Optimization Algorithm

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Qiang Wen Niu Wei Du Kai Wang Xiang-dong Yang Yong-an Du Wei-bing State Key Laboratory of Astronautic Dynamic

ISBN: (纸本)9781479919819

An improved autofocus method is presented based on minimum-entropy criterion for inverse synthetic aperture radar *** phase-error corrections are initialized by dominant scatter algorithm,and an iterative method is derived to minimize the image entropy.A local quadratic curve is constructed to gradually approach the extremun of the image entropy function,though minimizing the image entropy,the phase-errors which corrupt the image are ***,real ISAR data are used to validate the effectiveness and robustness of this *** results demonstrate that the proposed method has better effect of autofocusing under low SNR.

关键词： inverse synthetic aperture radar(ISAR) minimumentropy autofocus algorithm

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An Augmented Lagrangian Method for autofocused Compressed SAR Imaging 3

An Augmented Lagrangian Method for Autofocused Compressed SA...

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3rd International Workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)

作者： Gungor, Alper Cetin, Mujdat Guven, H. Emre ASELSAN Inc Air Platform Radar Syst Engn Ankara Turkey Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey ASELSAN Inc ASELSAN Res Adv Sensing Ankara Turkey

ISBN: (纸本)9781479974207

We present an autofocus algorithm for Compressed SAR Imaging. The technique estimates and corrects for 1-D phase errors in the phase history domain, based on prior knowledge that the reflectivity field is sparse, as in the case of strong scatterers against a weakly-scattering background. The algorithm relies on the Sparsity Driven autofocus (SDA) method and Augmented Lagrangian Methods (ALM), particularly Alternating Directions Method of Multipliers (ADMM). In particular, we propose an ADMM-based algorithm that we call autofocusing Iteratively Re Weighted Augmented Lagrangian Method (AIRWALM) to solve a constrained formulation of the sparsity driven autofocus problem with an fp-norm, p < 1 cost function. We then compare the performance of the proposed algorithm's performance to Phase Gradient autofocus (PGA) and SDA [2] in terms of autofocusing capability, phase error correction, and computation time.

关键词： Lagrangian field theory compressed sensing electromagnetic wave scattering error correction iterative methods radar imaging synthetic aperture radar 1D phase errors ADMM based algorithm AIRWALM PGA SDA method alternating directions method of multipliers autofocus algorithm autofocused compressed SAR imaging autofocusing iteratively reweighted augmented Lagrangian method phase error correction phase gradient autofocus phase history domain reflectivity field sparsity driven autofocus method weakly-scattering background Compressed sensing Optimization Radar polarimetry Radar remote sensing Synthetic aperture radar Compressed sensing Synthetic aperture radar Radar polarimetry Lagrangian field theory Radar remote sensing Electromagnetic wave scattering phase error correction Radar imaging Prostaglandins A Lagrangian method error correction iterative methods auto-focus

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