Optical imaging process based on two-dimensional Fourier transform for synthetic aperture imaging ladar

By extending synthetic aperture technology from a microwave band to laser wavelength, the synthetic aperture ladar (SAL) achieves extremely high spatial resolution independent of the target distance in long-range imaging. Nonlinear phase correction is a critical challenge in SAL imaging. To address the issue of phase noise during the imaging process, we first analyze the theoretical impact of nonlinear phase noise in imaging performance. Subsequently, a reconstruction and compensation method based on orthonormal complete basis functions is proposed to mitigate nonlinear phase noise in SAL imaging. The simulation results validate the accuracy and robustness of the proposed method, while experimental data demonstrate its effectiveness in improving system range resolution and reducing the peak side lobe ratio by 3 dB across various target scenarios. This advancement establishes a solid foundation for the application of SAL technology in ground-based remote sensing and space target observation.

Synthetic aperture imaging ladar (SAIL) is one of the most possible optical active imaging methods to break the diffraction limit and achieve super-resolution in a long distance. Nevertheless, two-dimensional reconstructed images of the natural targets have not been achieved. Polarization state change of the backscattered light, which is always determined by the interaction of the light and the materials on the target plane, will affect the imaging of SAIL. The Mueller matrices can describe the complex polarization features of the target reflection and treat this interaction. In this paper, a measurement of the Mueller matrices for different target materials will be designed, and the influences of polarization characteristic of targets on resolution element imaging in side-looking and down-looking SAILs will be theoretically analyzed.

As synthetic aperture imaging ladar employs the linear chirp laser signal, it is inevitably impacted by the space-time varying speckle effect. In many SAIL two-dimensional reconstructed images, the laser speckle effect severely reduces the image quality. In this paper, we analyze and simulate the influence of space-time speckle effect to the resolution element imaging both in range direction and in azimuth direction. Expressions for two-dimensional data collection contained space-time speckle effect are obtained, and computer simulation results of resolution degradation both in range direction and in cross-range direction are presented.

In conventional imaging laser radar, the resolution of target is constrained by the diffraction-limited, which includes the beamwidth of the laser in the target plane and the telescope's aperture. Synthetic aperture imaging Ladar (SAIL) is an imaging technique which employs aperture synthesis with coherent laser radar, the resolution is determined by the total frequency spread of the source and is independent of range, so can achieve fine resolution in long range. Ray tracing is utilized here to obtain two-dimensional scattering properties from three-dimensional geometric model of actual target, and range-doppler algorithm is used for synthetic aperture process in laser image simulation. The results show that the SAIL can support better resolution.

Neural network based synthetic aperture ladar imaging through marine atmosphere

合成孔径激光成像雷达中非线性啁啾补偿算法的误差分析

In this paper, the effect of coherent acoustic wave excitation system on the echo signal detection of Synthetic Aperture Ladar (SAL) is studied. The local atmospheric refractive index fluctuation model of the disturbed troposphere is established by the coherent acoustic wave theory, and the detection process of target echo signal is simulated. The influence of the phase fluctuation of the disturbance echo signal on the intermediate frequency signal of the coherent acoustic wave excited system is studied. By employing the coherent acoustic wave excitation system interfering with the Michelson laser interferometer imaging, the effect of the coherent acoustic wave on the phase fluctuation of the echo signal is explained theoretically. The results of this study lay a theoretical foundation for using coherent acoustic excitation system against SAL imaging.

Ladar system simulation is to simulate the ladar models using computer simulation technology in order to predict the performance of the ladar system. This paper presents the developments of laser imaging radar simulation for domestic and overseas studies and the studies of computer simulation on ladar system with different application requests. The LadarSim and FOI-LadarSIM simulation facilities of Utah State University and Swedish Defence Research Agency are introduced in details. This paper presents the low level of simulation scale, un-unified design and applications of domestic researches in imaging ladar system simulation, which are mostly to achieve simple function simulation based on ranging equations for ladar systems. Design of laser imaging radar simulation with open and modularized structure is proposed to design unified modules for ladar system, laser emitter, atmosphere models, target models, signal receiver, parameters setting and system controller. Unified Matlab toolbox and standard control modules have been built with regulated input and output of the functions, and the communication protocols between hardware modules. A simulation based on ICCD gain-modulated imaging ladar system for a space shuttle is made based on the toolbox. The simulation result shows that the models and parameter settings of the Matlab toolbox are able to simulate the actual detection process precisely. The unified control module and pre-defined parameter settings simplify the simulation of imaging ladar detection. Its open structures enable the toolbox to be modified for specialized requests. The modulization gives simulations flexibility.

We demonstrate that a significant improvement can be obtained in the recognition of complicated synthetic aperture radar images taken from the Moving and Stationary Target Acquisitions and Recognition database. These images typically have a low number of scattering centers and high noise. We first preprocess the images and the templates formed from them so that their scattering centers are enhanced. Our technique can produce high-quality performance in several correlation criteria. For realistic automatic target recognition systems, our approach should make it easy to implement optical recognition systems with binarized data for many different types of correlation filter and should have a great effect on feeding data-compressed (binarized) information into either digital or optical processors.

Image segmentation has recently become an essential step in image processing as it mainly conditions the interpretation which is done afterwards. It is still difficult to justify the accuracy of a segmentation algorithm, regardless of the nature of the treated image. In this paper we perform an objective comparison of region-based segmentation techniques such as supervised and unsupervised deterministic classification, non-parametric and parametric probabilistic classification. Eight methods among the well-known and used in the scientific community have been selected and compared. The Martin's(GCE, LCE), probabilistic Rand Index (RI), Variation of Information (VI) and Boundary Displacement Error (BDE) criteria are used to evaluate the performance of these algorithms on Magnetic Resonance (MR) brain images, synthetic MR image, and synthetic images. MR brain image are composed of the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and others, and the synthetic MR image composed of the same for real image and the plus edema, and the tumor. Results show that segmentation is an image dependent process and that some of the evaluated methods are well suited for a better segmentation. Keywords—Evaluation criteria; Martin's; Rand Index; Image Segmentation; Magnetic resonance image. I. INTRODUCTION

Hidden object imaging is challenging problem in the fields of computer vision and image processing, and it’s a key step in many application fields, include intelligent video surveillance, visual tracking and scene understanding. Recently, the camera array synthetic aperture imaging has been proved to be a powerful technology for hidden object detection, and the state-of-art synthetic imaging method can focus on multiple parallel planes so as to achieve seeing hidden through severe occlusion. However, due to the depth variation of hidden object’s surface, it’s difficult for existing method to get a complete clear image. This paper presents a novel synthetic aperture imaging and fusion approach, the main characteristics of proposed method include three parts: (1) A novel imaging framework is presented which integrate synthetic aperture imaging and fusion seamlessly. (2) Several multiple scale image fusion methods have been adopted in our framework to create high resolution and highly detailed images of occluded object. (3)A camera array synthetic aperture imaging and fusion system has been developed based on the proposed approach, and the Stanford light field public dataset and our camera array occlusion object dataset have been used to evaluate the performance of this algorithm. Extensive experiment results demonstrate that compared to the existing state-of-art camera array synthetic aperture imaging approach, our approach can enhance the image detail and quality of hidden objects under occlusion.

A novel parallel model of natural language (NL) understanding is presented which can realize high levels of semantic abstraction, and is designed for implementation on synchronous SIMD architectures and optical processors. Theory is expressed in terms of the Image Algebra (IA), a rigorous, concise, inherently parallel notation which unifies the design, analysis, and implementation of image processing algorithms. The IA has been implemented on numerous parallel architectures, and IA preprocessors and interpreters are available for the FORTRAN and Ada languages. In a previous study, we demonstrated the utility of IA for mapping MEA- conformable (Multiple Execution Array) algorithms to optical architectures. In this study, we extend our previous theory to map serial parsing algorithms to the synchronous SIMD paradigm. We initially derive a two-dimensional image that is based upon the adjacency matrix of a semantic graph. Via IA template mappings, the operations of bottom-up parsing, semantic disambiguation, and referential resolution are implemented as image-processing operations upon the adjacency matrix. Pixel-level operations are constrained to Hadamard addition and multiplication, thresholding, and row/column summation, which are available in magnitude-only optics. Assuming high parallelism in the parse rule base, the parsing of n input symbols with a grammar consisting of M rules of arity H, on an N-processor architecture, could exhibit time complexity of T(n) <EQ O(MHn/N). When N equals O (Mn), which is feasible with massive parallelism, the computational cost is constant and of order H. Since H < < n is typical, we claim a fundamental complexity advantage over the current O(n) theoretical time limit of MIMD parsing architectures. Additionally, we show that inference over a semantic net is achievable is parallel in O(m) time, where m corresponds to the depth of the search tree. Results are evaluated in terms of computational cost on SISD and SIMD processors, with discussion of implementation on electro-optic architectures.

We present a nonlinear optical processor that can perform image deconvolution in real time. This processor may be used to remove the distortion effects from an optical image, such as restoration of a smeared or out-of-focus photographic image. In this technique, both the degraded image and the distorting function are displayed side by side at the input plane of the optical processor. The Fourier transform interference intensity is obtained using a Fourier transform lens and a square law device such as a CCD array. A hard clipping nonlinearity is used to threshold the modified Fourier transform interference intensity to only two values, 1 and − 1. The effect of the nonlinearity is to remove the distorted amplitude entirely and to restore the correct phase of the original signal. An amplitude mask averaged over an ensemble of images is used at the Fourier plane to provide the amplitude spectrum. An inverse Fourier transform of the average amplitude spectrum with the recovered correct phase information can yield the original image with considerably reduced distortion.

Design and Implementation of Fractional Order Integral Filter for Denoising of Echocardiographic Images

Binary image algebra and optical cellular logic processor design