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compressive sensing for small moving space object detection in astronomical images

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Journal of Systems Engineering and Electronics 2012年第3期23卷 378-384页

作者： Rui Yao Yanning Zhang Shaanxi Key Laboratory of Speech&Image Information Processing School of Computer ScienceNorthwestern Polytechnical UniversityXi'an 710129P.R.China

It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.

关键词： compressive sensing small space object detection localization astronomical image.

compressive sensing Inverse Synthetic Aperture Radar Imaging Based on Gini Index Regularization

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International Journal of Automation and computing 2014年第4期11卷 441-448页

作者： Can Feng Liang Xiao Zhi-Hui Wei School of Computer Science and Engineering Nanjing University of Science & Technology North Information Control Group Co. Ltd.

In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar(ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation(SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio(PSLR) and the reconstruction relative error(RE) indicate that the proposed method outperforms the l1 norm based method.

关键词： compressive sensing inverse synthetic aperture radar (ISAR) imaging sparsity Gini index regularization

compressive sensing Reconstruction Based on Weighted Directional Total Variation

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Journal of Shanghai Jiaotong university(Science) 2017年第1期22卷 114-120页

作者：闵莉花冯灿 School of Science Nanjing University of Posts and Telecommunications Department of Beidou North Information Control Research Academy Group Co.Ltd.

Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.

关键词： compressive sensing weighted directional total variation majorization-minimization algorithm TP 391.4 A

compressive sensing SAR range compression with chirp scaling principle

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Science China(Information Sciences) 2012年第10期55卷 2292-2300页

作者： XIAO Peng,YU Ze & LI ChunSheng School of Electronics and Information Engineering,Beihang University,Beijing 100191,China School of Electronics and Information Engineering Beihang University Beijing China

compressive sensing(CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of *** paper proposes a novel SAR range compression,namely compressive sensing with chirp scaling(CS-CS),achieving the same range resolution as conventional SAR approach,while using fewer range *** order to realize accurate range cell migration correction(RCMC),chirp scaling principle is used to construct reference matrix for compressive sensing ***,error diagrams are designed for measurement of the performance of CS-CS,and some experiments of using real data are performed to deal with the errors caused by three conditions:SNR,sparsity and sampling.

关键词： synthetic aperture radar compressive sensing range cell migration correction error radar signal processing

compressive sensing in wireless sensor network for poultry acoustic monitoring

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International Journal of Agricultural and Biological Engineering 2017年第2期10卷 94-102页

作者： Xuan Chuanzhong Wu Pei Zhang Lina Ma Yanhua Liu Yanqiu Maksim College of Mechanical and Electrical Engineering Inner Mongolia Agricultural UniversityHohhot 010018China College of Physics and Electronic Information Science Inner Mongolia Normal UniversityHohhot 010022China

A wireless acoustic sensor network was realized using wireless sensor nodes equipped with microphone condensers,in which its sensor nodes were configured to capture poultry sound data and transmit it via the network to a collection point.A high performance computer can process these large volumes of animal audio signals under different *** performing data signal processing and analyzing the audio signal,poultry sound can be achieved and then transformed into their corresponding behavioral modes for welfare *** this study,compressive sensing algorithm was developed in consideration of the balance between the power saving from compression ratio and the computational cost,and a low power consumption as well as an inexpensive sensor node was designed as the elementary unit of poultry acoustic data collecting and ***,a Zigbee-based wireless acoustic sensor network was developed to meet the challenges of short transmission range and limited resources of storage and *** results demonstrate that the compressive sensing algorithm can improve the communication performances of the wireless acoustic sensor network with high reliability,low packet loss rate and low energy consumption.

关键词： wireless sensor network compressive sensing poultry acoustic monitoring poultry sound data power consumption acoustic data compression

Efficient compressive sensing tracking via mixed classifier decision

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Science China(Information Sciences) 2016年第7期59卷 143-157页

作者： Hang SUN Jing LI Jun CHANG Bo DU Zhenyang SU Computer School Wuhan University

Recent years have witnessed successful use of tracking-by-detection methods, with a number of promising results being achieved. Most of these algorithms use a sliding window to collect samples and then employ these samples to train and update the classifiers. They also use an updated classifier to establish the appearance model and they take the maximum response value of the classifier as the location of the target within a fixed radius. compressive Tracking(CT) is a novel tracking-by-detection algorithm that updates the appearance model in a compressed domain. However, the conventional CT algorithm uses a single classifier to detect the target, and if the selected region drifts, the classifier may become inaccurate. Furthermore, the CT algorithm updates the classifier parameters with a constant learning rate. Therefore, if the target is completely occluded for an extended period, the classifier will instead learn the features of the covered object and the target will ultimately be lost. To overcome these problems, we present a compressive sensing tracking algorithm using mixed classifier decision. The main improvements in our algorithm are that it adopts mixed classifiers to locate the target and it applies a dynamic learning rate to update the appearance model. An experimental comparison with state-of-the-art algorithms on eight benchmark video sequences in complicated situations shows that the proposed algorithm achieves the best performance with 12 pixels on the average center location error and 66.82%on the average overlap score.

关键词： compressive sensing object tracking mixed classifier decision dynamic learning rate appearance model update

A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

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Chinese Physics B 2015年第6期24卷 198-206页

作者：肖迪蔡洪坤郑洪英 Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University) of Chinese Ministry of Education College of Computer ScienceChongqing University

In this paper, a compressive sensing （CS） and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc.

关键词： joint encryption and watermarking compressive sensing chaos Arnold map

Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

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Chinese Physics B 2023年第2期32卷 136-149页

作者：王兴元王哓丽滕琳蒋东华咸永锦 School of Information Science and Technology Dalian Maritime UniversityDalian 116026China Guangxi Key Laboratory of Multi-source Information Mining&Security Guangxi Normal UniversityGuilin 541004China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 511400China School of Cyber Security Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

关键词： chaotic image encryption compressive sensing meaningful cipher image portable network graphics image encryption algorithm

Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

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Frontiers of Computer Science 2023年第3期17卷 161-175页

作者： Xiaoling HUANG Youxia DONG Guodong YE Yang SHI Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China School of Software Engineering Tongji UniversityShanghai 200092China

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public *** make the chaotic sequence more random,a mathematical model is constructed to improve the random ***,the plain image is compressed and encrypted to obtain the secret ***,the secret image is inserted with numbers zero to extend its size same to the plain *** applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier ***,a meaningful carrier image embedded with secret plain image can be obtained by inverse ***,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic ***,information entropy of the plain image is employed to produce the initial conditions of chaotic *** a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the *** simulations show that the normalized correlation(NC)values between the host image and the cipher image are *** is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

关键词： image encryption algorithm compressive sensing integer wavelet transform Hadamard matrix