Aming at measure the rapid target’s spatio, temporal and spectrum characteristics data synchronously, we propose a joint spatio-temporal-spectrum super-resolution method for compressive sensing optical imaging system based DMD. Based on the CACTI and CASSI technology, we focus on the key technologies such as multi-scale joint coding and time-space-frequency multi-dimensional light field joint reconstruction algorithm using DMD. Though the accuracy of photometric characteristic data is not accurate, it can meet the requirements of non-cooperative targets emergency measurement application.
Compressed sensing theory is a new sampling theory, which provides a method to recover the original signal from a small number of samples. For sparse signal and compressible signal, compressed sensing theory compresses the signal while sampling. It combines the sampling process and compression process. It breaks through the traditional Nyquist sampling law and saves a lot of storage, transmission, computing and other resources. This theory not only reduces the cost of storage and transmission of digital image and video acquisition, but also provides a new opportunity for the follow-up research of image processing and recognition, and promotes the combination of theory and engineering application. It includes three parts: sparse representation of target, design of measurement matrix and reconstruction of target. Reconstruction algorithm is a key step in the process of compression imaging, which determines the accuracy and speed of image reconstruction to a certain extent, so it is very important to select the appropriate image quality evaluation index. The image quality evaluation of existing reconstruction algorithms mainly focuses on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The advantages of these two algorithms lie in simple algorithm, fast inspection speed, which are suitable for evaluating the advantages and disadvantages of algorithms, but the disadvantage is that they can only be evaluated on the basis of known original images. In the actual imaging process of compressed sensing, it is impossible to obtain the original image, so we need to use an image quality evaluation method which is not based on the original image.
The existing space situation awareness optical imaging system is limited by the satellite platform and optical system, and it is difficult to realize large aperture observation and multi-dimensional optical characteristics data acquisition for rapid target detection. Aiming at the problem that the sparse aperture system is difficult to achieve clear imaging in all depth of field, and the image quality degradation caused by the defocusing and dislocation of the object point and phase closure, the refocusing imaging technology based on light field modulation is adopted to expand the synthetic aperture to full depth of field, and effectively compress the amount of data.
Space target detection and recognition is the premise of competing for the advantage of space information and the important data source of space situation awareness. With the increasing space activities and threats such as space rendezvous and emergency launch, the demand for emergency detection and identification of space targets is becoming more and more urgent, and the demand for fast emergency and high-precision measurement of optical characteristics of space targets is also put forward. This paper introduces the space target observation information processing system based on camera array, which is being developed. The system is mainly used for space non cooperative target emergency measurement, and mainly includes four parts: firstly, the hardware basis of the whole information system is camera array optical measurement system; secondly, the system is based on STK and MATLAB two software joint platform, through the interface provided by STK/Connect and MATLAB interactive connection, through the MATLAB commands and instruction set to achieve the interactive use of data between the two software, as well as the collection and creation of charts and reports; third, the key technology of software development, namely the main function module The module mainly includes: initial orbit determination, orbit prediction, space target feature extraction based on optical characteristics, and space target recognition; finally, the whole software system is developed through the integration of the above four functional modules
Images store a lot of information and are the window for human beings to understand things. A lot of research is devoted to analyzing and processing images, which is called image processing in a broad sense. Image processing includes image recognition, image restoration, image enhancement, image coding and so on. This paper mainly focuses on the field of image restoration. Image restoration, also known as image inverse problem, aims to restore high-quality original images from degraded or damaged observations. It also acts as a preprocessing step in many intermediate and advanced image processing tasks. Due to the limitations of sensors or environmental conditions, imaging systems usually have factors such as noise, optical or motion blur, resulting in image degradation and distortion. Aiming at the ill posed problem of image pixel missing and blur in the process of compression coding, this paper uses GMM model to solve the degraded image, so as to achieve the purpose of image restoration.
Imaging lidar is widely used in the fields of national defense, military affairs and people's livelihood because of its high resolution and fast imaging speed. When the detection distance is large, the echo is relatively weak, and the echo photons impinging into each pixel of the detector array is very few, even single photon. The appearance of single photon imaging technology solves this problem. Single-photon imaging detection technology has attracted much attention as a new detection technology. In recent years, single-photon imaging radar have developed rapidly. The article introduces the working principle and key technology of single-photon imaging detection, and summarizes the development of single-photon radar. Finally, the article discusses the application of single-photon imaging detection in various fields in the future.
In the spatial target surveillance and astronomical observation applications, image matching processing is the key procedure for the multi-temporal starry images or the multi-channel starry images acquired by different imaging sensors. However, the starry images obtained often have low Signal-to-Noise Ratios (SNR), the light intensities of the target stars or spacecrafts in them are vulnerable to background interferences, such as the atmospheric turbulence and the night clouds, etc., and become dim and instable. With the weak texture information of the target stars, all the influences make the feature point extraction quite difficult. In this paper, a new type of image matching method based on the description of Multi-scale Geometric Invariant Features (MGIF) is proposed, which uses the Rolling Guidance Filter (RGF) to perform preprocessing for the input images. By virtue of the excellent edge-preserving performance of the Joint Bilateral Filter in RGF, the integrities of contour profile of the star points are guaranteed effectively while the interference and other noise in the background are suppressed. Then the segmented and morphology methods are applied to extract star points and get the centroid of star points to form the feature point constellation. Considering the cross ratio of two lines in projection transformation model of image matching is a geometric invariant, a multi-scale geometric invariants based function, which uses the scaling of RGF as a reference to describe the relative spatial positions of matching points more accurately, is constructed to evaluate the level of similarity between star points according to the relative position of each points in the constellation. Subsequently, Random Sample Consensus(RANSAC)method is adopted to remove the mismatching star points and calculate the rigid transform matrix and other registration parameters. Digital simulation and practical processing results demonstrate that the proposed method can achieve higher matching accuracy and robustness for the starry images with low SNR and complex backgrounds.
Astronomical observation and spatial target surveillance applications often require mosaic processing of starry images acquired by multiple image sensors to expand the Fields of View (FOV) or improve the resolutions. Due to the low SNR (Signal-to-Noise Ratio), lack of star point texture information and vulnerability of atmospheric turbulence of the starry image properties, traditional mosaic methods are prone to failures during feature point extraction. In this paper, Spatio-Temporal Context (STC) filtering is introduced as the preprocessing procedure to suppress the background interferences. We have improved the classical STC filtering and expands it into multi-scale space combining with Rolling-Guidance Filtering Algorithm (RGFA). Making full use of the fine edge-preserving feature of RGFA, the time-variant or spatial variant interference and noise in the background, such as glimmer stars, night clouds, sensor response noise, etc, are suppressed while the profiles of the target star points are enhanced and easy to extract their centroids. Then, we produced the feature description of the star-point sets via threshold segmentation and morphological algorithms based on geometric invariant cost function for the input image pairs to be stitched. After Random Sample Consensus (RANSAC) processing, the mismatched feature point pairs in the star-point sets are excluded. The subsequent procedures of the registration parameter calculation, image fusion and parallax correction processing are adopted to complete the mosaic processing. The results of digital simulation and practical processing show that the proposed method for the multichannel sequence starry images with the low SNR and complex backgrounds can extract feature points more precisely and more robustly comparing with the traditional methods. So, it is suitable for the large FOV spatial observation or surveillance applications.
The dim target tracking is essential for the spatial surveillance system. Considering that the starry image sequences acquired by imaging sensors often has low Signal-to-Noise Ratio (SNR), the brightness of a spatial target is often susceptible to the background interferences, such as the night clouds and the atmospheric turbulence, etc, and become dim and instable, its shape and profile is also blurred and lack of texture information. In order to extract the target from background, Spatio-Temporal Context Model (STCM) based filtering theory is applied in this paper and used to improve the traditional Kernelized-Correlation-Filter (KCF) target tracking method. It introduces a spatial weighting function that can pre-enhance the point target and suppresses the background interferences. So the tracking drift phenomenon is relieved when the moving object being obstructed temporarily. Considering that L1 regularization is easier to obtain sparse solutions and L2 regularization has smoothness property, the regularization function of the regressive classifiers in KCF target tracking method is renewed by using variable L1 or L2 regularization instead. The index of regularization in the improved regression model is a piecewise function, which is determined by the cost function during learning period that can distinguish the target star point from the background point by using the characteristics of points (such as brightness, etc.)The numeral simulation and actual processing results show that, comparing with the traditional Kernelized- Correlation-Filter (KCF) methods, the proposed method owns more robustness and precision in the starry images with low signal-to-noise ratio and complex background.
Aiming at improving spatial resolution and dynamic range of video for real-world applications, this paper focuses on the joint super-resolution, high dynamic range image reconstruction algorithm for multiple images captured by single camera. On the assumption that the response function is same at each pixel with different exposures, the joint super resolution and high dynamic range technology can be implemented. We propose a joint super-resolution and high dynamic range imaging algorithm reconstructed from multi-exposure images simultaneously. We conduct experiments under static and dynamic scene to validate the robustness of the proposed approach. Subjective and objective assessments for various experiments are presented to validate the effectiveness of the proposed super-resolution and high dynamic range reconstruction algorithm.
To address the problem of low resolution of infrared imaging system, this paper combined compression coded aperture imaging to study infrared imaging, which can break through the imaging limit of infrared detectors and achieve super-resolution imaging. Compression coded aperture imaging mainly utilizes the sparsity of images, and solves mathematical models through reconstruction algorithms and reconstructs target images with high resolution. Reconstruction algorithm is a vital procedure in the process of compression coded aperture imaging, which determines the reconstruction accuracy and reconstruction speed of the image to some extent. In this paper, the existing compression coded aperture imaging reconstruction algorithms are classified and summarized. In the infrared imaging, the typical algorithm is simulated and verified, which can provide reference for future research in the field of infrared imaging.
Multi video super-resolution algorithms reconstruct high spatio-temporal resolution video by exploiting complementary information in multiple low-resolution video sequences. Aiming at improving spatio-temporal resolution of video for real-world applications, an algorithm is proposed using Maximum Posterior Likelihood - Markov Random Field (MAP-MRF) and implemented on camera array. Compared with the current algorithms for super-resolution reconstruction, the suggested algorithm is advantageous in keeping the edge sharpness and detailed texture, and robust against the noises. The experimental result has confirmed the effectiveness of the proposed method under the practical conditions such as large displacement and motion aliasing.
High dynamic image is an important technology of photoelectric information acquisition, providing higher dynamic range and more image details, and it can better reflect the real environment, light and color information. Currently, the method of high dynamic range image synthesis based on different exposure image sequences cannot adapt to the dynamic scene. It fails to overcome the effects of moving targets, resulting in the phenomenon of ghost. Therefore, a new high dynamic range image acquisition method based on multiplex cameras system was proposed. Firstly, different exposure images sequences were captured with the camera array, using the method of derivative optical flow based on color gradient to get the deviation between images, and aligned the images. Then, the high dynamic range image fusion weighting function was established by combination of inverse camera response function and deviation between images, and was applied to generated a high dynamic range image. The experiments show that the proposed method can effectively obtain high dynamic images in dynamic scene, and achieves good results.
Optical measuring angle data can be used in initial orbit determination. However, optical system has its magnitude limit, the initial orbits can not be determined when targets’ magnitudes are above the limited magnitude or the relative size of the target can not meet the resolution requirements. In order to expand the observable range of optical system and improve the accuracy of the orbit, it is necessary to improve the limited magnitude and the limited resolution of the system. This paper discusses the feasibility of initial orbit determination using camera array and provide the core processes of initial orbit determination using camera array. The experimental results show the effectiveness of the camera array to improve the system’s limited magnitude and the limited resolution.
Aiming to achieve the spatio-temporal alignment of multi sensor on the same platform for space target observation, a joint spatio-temporal alignment method is proposed. To calibrate the parameters and measure the attitude of cameras, an astronomical calibration method is proposed based on star chart simulation and collinear invariant features of quadrilateral diagonal between the observed star chart. In order to satisfy a temporal correspondence and spatial alignment similarity simultaneously, the method based on the astronomical calibration and attitude measurement in this paper formulates the video alignment to fold the spatial and temporal alignment into a joint alignment framework. The advantage of this method is reinforced by exploiting the similarities and prior knowledge of velocity vector field between adjacent frames, which is calculated by the SIFT Flow algorithm. The proposed method provides the highest spatio-temporal alignment accuracy compared to the state-of-the-art methods on sequences recorded from multi sensor at different times.
Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.
In order to resolve the difficult problem of detection and identification of optical targets in complex background or in long-distance transmission, this paper mainly study the range profiles of “cat-eye” targets using bi-spectrum. For the problems of laser echo signal attenuation serious and low Signal-Noise Ratio (SNR), the multi-pulse laser signal echo signal detection algorithm which is based on high-order cumulant, filter processing and the accumulation of multi-pulse is proposed. This could improve the detection range effectively. In order to extract the stable characteristics of the one-dimensional range profile coming from the cat-eye targets, a method is proposed which extracts the bi-spectrum feature, and uses the singular value decomposition to simplify the calculation. Then, by extracting data samples of different distance, type and incidence angle, verify the stability of the eigenvector and effectiveness extracted by bi-spectrum.
Space targets in astronomical images such as spacecraft and space debris are always in the low level of brightness and hold a small amount of pixels, which are difficult to distinguish from fixed stars. Because of the difficulties of space target information extraction, dynamic object monitoring plays an important role in the military, aerospace and other fields, track extraction of moving targets in short-exposure astronomical images holds great significance. Firstly, capture the interesting stars by region growing method in the sequence of short-exposure images and extract the barycenter of interesting star by gray weighted method. Secondly, use adaptive threshold method to remove the error matching points and register the sequence of astronomical images. Thirdly, fuse the registered images by NCST-PCNN image fusion algorithm to hold the energy of stars in the images. Fourthly, get the difference of fused star image and final star image by subtraction of brightness value in the two images, the interesting possible moving targets will be captured by energy accumulation method. Finally, the track of moving target in astronomical images will be extracted by judging the accuracy of moving targets by track association and excluding the false moving targets. The algorithm proposed in the paper can effectively extract the moving target which is added artificially from three images or four images respectively, which verifies the effectiveness of the algorithm.
As the commercial performance of camera sensor and the imaging quality of lens improving, it has the possibility to applicate in the space target observation. Multiple cameras can further improve the detection ability of the camera with image fusion. This paper mainly studies the multiple camera image fusion problem of registration with the imaging characteristics of a commercial camera, and then put forward an applicable method of star image registration. It proved that the accuracy of registration could reach the subpixel level with experiments.
The laser stealth of space target is useful, important and urgent in practice. This paper introduces the definition expression of laser radar cross section (LRCS) and the general laws of the influencing factors of space target’s LRCS, including surface materials types, target’s shape and size. Then this paper discusses the possible laser stealth methods of space target in practical applications from the two view points of material stealth methods and shape stealth methods. These conclusions and suggestions can provide references for the next research thinking and methods of the target’s laser stealth.
KEYWORDS: Signal to noise ratio, Signal detection, Electronic filtering, Optical filters, Laser processing, Signal processing, Interference (communication), Laser applications, Laser range finders, Filtering (signal processing)
The multi-pulsed laser ranging technology is prominent on improving the maximum measuring range of laser active detection,laser range finder and other long-distance measurement. For all laser echo detection techniques, the weak signal detection is an important step, which aims to increase the detection range. Most algorithms are based on the priori knowledge of laser echo or the improvement of laser power. However, we cannot know or estimate the waveform accurately in many applications. Moreover, these means are difficult to satisfy the real-time needs. The present paper proposes an improved algorithm which extended the signal accumulation algorithm for the high power burst laser. This method is mainly based on signal accumulation and tri-cumulant algorithm which can improve the signal to noise SNR of the weak laser echo; moreover it does not need more prior knowledge of echo. In order to reduce the detection time, the algorithm is realized based on FPGA using signal retiming and parallel pipeline structure. The simulations and experiments results demonstrate that the minimum detecting SNR is -5dB and the maximum detecting time is only less than 1us.
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