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Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding
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作者 王一铭 黄树锋 +2 位作者 陈煌 杨健 蔡述庭 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第1期287-302,共16页
A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete... A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality. 展开更多
关键词 visual security image encryption parallel compressive sensing edge detection embedding
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Chaotic CS Encryption:An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing
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作者 Mingliang Sun Jie Yuan +1 位作者 Xiaoyong Li Dongxiao Liu 《Computers, Materials & Continua》 SCIE EI 2024年第5期2625-2646,共22页
Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgori... Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors. 展开更多
关键词 image encryption chaotic system compressive sensing arnold transform
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Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System
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作者 Zhiqing Dong Zhao Zhang +1 位作者 Hongyan Zhou Xuebo Chen 《Computers, Materials & Continua》 SCIE EI 2024年第2期1977-1993,共17页
With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color image... With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective. 展开更多
关键词 image encryption image compression hyperchaotic system compressed sensing
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Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing 被引量:1
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作者 王兴元 王哓丽 +2 位作者 滕琳 蒋东华 咸永锦 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第2期136-149,共14页
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. F... 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. 展开更多
关键词 chaotic image encryption compressive sensing meaningful cipher image portable network graphics image encryption algorithm
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Three-Stages Hyperspectral Image Compression Sensing with Band Selection
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作者 Jingbo Zhang Yanjun Zhang +1 位作者 Xingjuan Cai Liping Xie 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第1期293-316,共24页
Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effecti... Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effectively alleviating the bandwidth pressure during data transmission.However,CS has many shortcomings in the transmission of hyperspectral image(HSI)data.This work aims to consider the application of CS in the transmission of hyperspectral image(HSI)data,and provides a feasible research scheme for CS of HSI data.HSI has rich spectral information and spatial information in bands,which can reflect the physical properties of the target.Most of the hyperspectral image compressed sensing(HSICS)algorithms cannot effectively use the inter-band information of HSI,resulting in poor reconstruction effects.In this paper,A three-stage hyperspectral image compression sensing algorithm(Three-stages HSICS)is proposed to obtain intra-band and inter-band characteristics of HSI,which can improve the reconstruction accuracy of HSI.Here,we establish a multi-objective band selection(Mop-BS)model,amulti-hypothesis prediction(MHP)model and a residual sparse(ReWSR)model for HSI,and use a staged reconstruction method to restore the compressed HSI.The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS,and it performs best among all comparison algorithms. 展开更多
关键词 Combinatorial optimization band selection hyperspectral image compressed sensing
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Optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system and compressed sensing
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作者 都洋 隆国强 +2 位作者 蒋东华 柴秀丽 韩俊鹤 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第11期426-445,共20页
Some existing image encryption schemes use simple low-dimensional chaotic systems, which makes the algorithms insecure and vulnerable to brute force attacks and cracking. Some algorithms have issues such as weak corre... Some existing image encryption schemes use simple low-dimensional chaotic systems, which makes the algorithms insecure and vulnerable to brute force attacks and cracking. Some algorithms have issues such as weak correlation with plaintext images, poor image reconstruction quality, and low efficiency in transmission and storage. To solve these issues,this paper proposes an optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system(4D MHS) and compressed sensing(CS). Firstly, this paper proposes a new 4D MHS, which has larger key space, richer dynamic behavior, and more complex hyperchaotic characteristics. The introduction of CS can reduce the image size and the transmission burden of hardware devices. The introduction of double random phase encoding(DRPE) enables this algorithm has the ability of parallel data processing and multi-dimensional coding space, and the hyperchaotic characteristics of 4D MHS make up for the nonlinear deficiency of DRPE. Secondly, a construction method of the deterministic chaotic measurement matrix(DCMM) is proposed. Using DCMM can not only save a lot of transmission bandwidth and storage space, but also ensure good quality of reconstructed images. Thirdly, the confusion method and diffusion method proposed are related to plaintext images, which require both four hyperchaotic sequences of 4D MHS and row and column keys based on plaintext images. The generation process of hyperchaotic sequences is closely related to the hash value of plaintext images. Therefore, this algorithm has high sensitivity to plaintext images. The experimental testing and comparative analysis results show that proposed algorithm has good security and effectiveness. 展开更多
关键词 MEMRISTOR hyperchaotic system compressed sensing fractional Fourier transform optical image encryption
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High Capacity Data Hiding in Encrypted Image Based on Compressive Sensing for Nonequivalent Resources 被引量:2
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作者 Di Xiao Jia Liang +2 位作者 Qingqing Ma Yanping Xiang Yushu Zhang 《Computers, Materials & Continua》 SCIE EI 2019年第1期1-13,共13页
To fulfill the requirements of data security in environments with nonequivalent resources,a high capacity data hiding scheme in encrypted image based on compressive sensing(CS)is proposed by fully utilizing the adapta... To fulfill the requirements of data security in environments with nonequivalent resources,a high capacity data hiding scheme in encrypted image based on compressive sensing(CS)is proposed by fully utilizing the adaptability of CS to nonequivalent resources.The original image is divided into two parts:one part is encrypted with traditional stream cipher;the other part is turned to the prediction error and then encrypted based on CS to vacate room simultaneously.The collected non-image data is firstly encrypted with simple stream cipher.For data security management,the encrypted non-image data is then embedded into the encrypted image,and the scrambling operation is used to further improve security.Finally,the original image and non-image data can be separably recovered and extracted according to the request from the valid users with different access rights.Experimental results demonstrate that the proposed scheme outperforms other data hiding methods based on CS,and is more suitable for nonequivalent resources. 展开更多
关键词 compressive sensing encrypted image data hiding PREDICTION ERROR nonequivalent RESOURCES
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Reduced Imaging Time and Improved Image Quality of 3D Isotropic T2-Weighted Magnetic Resonance Imaging with Compressed Sensing for the Female Pelvis
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作者 Hao Mei Feng Xiao Ming Deng 《Journal of Beijing Institute of Technology》 EI CAS 2023年第5期579-585,共7页
This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D... This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images. 展开更多
关键词 compressed sensing sampling perfection with application-oriented contrasts(SPACE)using variable flip angle evolutions three-dimensional(3D)imaging magnetic resonance imaging(MRI) PELVIS
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Compressive sensing for small moving space object detection in astronomical images
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作者 Rui Yao Yanning Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第3期378-384,共7页
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 computationall... 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. 展开更多
关键词 compressive sensing small space object detection localization astronomical image.
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A Compression Algorithm of Hyperspectral Remote Sensing Image Based on 3-D Wavelet-Fractal Coder
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作者 邹毅 潘伟 敖露 《Journal of Donghua University(English Edition)》 EI CAS 2010年第2期229-232,共4页
In this paper, the 3-D Wavelet-Fractal coder was used to compress the hyperspectral remote sensing image, which is a combination of 3-D improved set partitioning in hierarchical trees (SPIHT) coding and 3-D fractal ... In this paper, the 3-D Wavelet-Fractal coder was used to compress the hyperspectral remote sensing image, which is a combination of 3-D improved set partitioning in hierarchical trees (SPIHT) coding and 3-D fractal coding. Hyperspectral image date cube was first translated by 3-D wavelet and the 3-D fractal compression ceding was applied to lowest frequency subband. The remaining coefficients of higher frequency sub-bands were encoding by 3-D improved SPIHT. We used the block set instead of the hierarchical trees to enhance SPIHT's flexibility. The classical eight kinds of affme transformations in 2-D fractal image compression were generalized to nineteen for the 3-D fractal image compression. The new compression method had been tested on MATLAB. The experiment results indicate that we can gain high compression ratios and the information loss is acceptable. 展开更多
关键词 image compression three dimensional improved SPIHT fractal compression coding hyperspectral
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TV Sparsifying MR Image Reconstruction in Compressive Sensing
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作者 Yonggui Zhu Xiaolan Yang 《Journal of Signal and Information Processing》 2011年第1期44-51,共8页
In this paper, we apply alternating minimization method to sparse image reconstruction in compressed sensing. This approach can exactly reconstruct the MR image from under-sampled k-space data, i.e., the partial Fouri... In this paper, we apply alternating minimization method to sparse image reconstruction in compressed sensing. This approach can exactly reconstruct the MR image from under-sampled k-space data, i.e., the partial Fourier data. The convergence analysis of the fast method is also given. Some MR images are employed to test in the numerical experi-ments, and the results demonstrate that our method is very efficient in MRI reconstruction. 展开更多
关键词 Compressed sensing Magnetic RESONANCE image TOTAL VARIATION image RECONSTRUCTION
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Face hallucination via compressive sensing 被引量:1
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作者 杨学峰 程耀瑜 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2016年第2期149-154,共6页
Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compress... Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compressive sensing based face hallucination method is presented,which is comprised of three steps:dictionary learning、sparse coding and solving maximum a posteriori(MAP)formulation.In the first step,the K-SVD dictionary learning algorithm is adopted to obtain a dictionary which can sparsely represent high resolution(HR)face image patches.In the second step,we seek the sparsest representation for each low-resolution(LR)face image paches input using the learned dictionary,super resolution image blocks are obtained from the sparsest coefficients and dictionaries,which then are assembled into super-resolution(SR)image.Finally,MAP formulation is introduced to satisfy the consistency restrictive condition and obtain the higher quality HR images.The experimental results demonstrate that our approach can achieve better super-resolution faces compared with other state-of-the-art method. 展开更多
关键词 face image super-resolution image face hallucination compressive sensing(CS)
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Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction
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作者 周正东 余子丽 +1 位作者 张雯雯 管绍林 《Journal of Southeast University(English Edition)》 EI CAS 2016年第4期420-425,共6页
To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates(SPS) algorithm are proposed for the prior image constrained compres... To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates(SPS) algorithm are proposed for the prior image constrained compressed sensing(PICCS)-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection(FBP), the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively. 展开更多
关键词 spectral X-ray CT prior image compressed sensing optimization algorithm image reconstruction
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Correspondence normalized ghost imaging on compressive sensing 被引量:2
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作者 赵生妹 庄鹏 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第5期287-291,共5页
Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibili... Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios (SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing (CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing (CS) and time-correspondence imaging (CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale "double-slit" image, the mean square error (MSE) by GI and the normalized GI (NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021 by CCNGI scheme with 2500 measurements. For the eight-grayscale "lena" object, the peak signal-to-noise rates (PSNRs) are 10.506 and 13.098, respectively using G1 and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale "double-slit" object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints (GISC) with a shorter reconstruction time. 展开更多
关键词 ghost imaging compressive sensing time-correspondence NORMALIZING
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Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm 被引量:2
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作者 Haipeng Zhang Ke Li +2 位作者 Changzhe Zhao Jie Tang Tiqiao Xiao 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第6期349-357,共9页
Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident... Towards efficient implementation of x-ray ghost imaging(XGI),efficient data acquisition and fast image reconstruction together with high image quality are preferred.In view of radiation dose resulted from the incident x-rays,fewer measurements with sufficient signal-to-noise ratio(SNR)are always anticipated.Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously.In this paper,a method based on a modified compressive sensing algorithm with conjugate gradient descent method(CGDGI)is developed to solve the problems encountered in available XGI methods.Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI.The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI. 展开更多
关键词 x-ray ghost imaging modified compressive sensing algorithm real-time x-ray imaging
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A LOSSLESS COMPRESSION ALGORITHM OF REMOTE SENSING IMAGE FOR SPACE APPLICATIONS 被引量:3
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作者 Sui Yuping Yang Chengyu +3 位作者 Liu Yanjun Wang Jun Wei Zhonghui He Xin 《Journal of Electronics(China)》 2008年第5期647-651,共5页
A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of ... A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio (CR) of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications. 展开更多
关键词 Remote sensing image Lossless compression Rice entropy coder Integer Discrete Wavelet Transform (DWT)
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Low complexity DCT-based distributed source coding with Gray code for hyperspectral images 被引量:1
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作者 Rongke Liu Jianrong Wang Xuzhou Pan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第6期927-933,共7页
To compress hyperspectral images, a low complexity discrete cosine transform (DCT)-based distributed source coding (DSC) scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize tr... To compress hyperspectral images, a low complexity discrete cosine transform (DCT)-based distributed source coding (DSC) scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain. Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-(LDPC)-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared imaging spectrometer (AVIRIS) dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage. 展开更多
关键词 image compression hyperspectral images distributed source coding (DSC) discrete cosine transform (DCT) Gray code band-interleaved-by-pixel (BIP).
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An Improved EZW Hyperspectral Image Compression 被引量:2
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作者 Kai-Jen Cheng Jeffrey C. Dill 《Journal of Computer and Communications》 2014年第2期31-36,共6页
The paper describes an efficient lossy and lossless three dimensional (3D) image compression of hyperspectral images. The method adopts the 3D spatial-spectral hybrid transform and the proposed transform-based coder. ... The paper describes an efficient lossy and lossless three dimensional (3D) image compression of hyperspectral images. The method adopts the 3D spatial-spectral hybrid transform and the proposed transform-based coder. The hybrid transforms are that Karhunen-Loève Transform (KLT) which decorrelates spectral data of a hyperspectral image, and the integer Discrete Wavelet Transform (DWT) which is applied to the spatial data and produces decorrelated wavelet coefficients. Our simpler transform-based coder is inspired by Shapiro’s EZW algorithm, but encodes residual values and only implements dominant pass incorporating six symbols. The proposed method will be examined on AVIRIS images and evaluated using compression ratio for both lossless and lossy compression, and signal to noise ratio (SNR) for lossy compression. Experimental results show that the proposed image compression not only is more efficient but also has better compression ratio. 展开更多
关键词 WAVELET TRANSFORM Karhunen-Loève TRANSFORM Transform-based image Compression AVIRIS hyperspectral image Embedded ZEROTREE WAVELET
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Stability of Efficient Deterministic Compressed Sensing for Images with Chirps and Reed-Muller Sequences 被引量:1
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作者 Somantika Datta Kangyu Ni +1 位作者 Prasun Mahanti Svetlana Roudenko 《Applied Mathematics》 2013年第1期183-196,共14页
We explore the stability of image reconstruction algorithms under deterministic compressed sensing. Recently, we have proposed [1-3] deterministic compressed sensing algorithms for 2D images. These algorithms are suit... We explore the stability of image reconstruction algorithms under deterministic compressed sensing. Recently, we have proposed [1-3] deterministic compressed sensing algorithms for 2D images. These algorithms are suitable when Daubechies wavelets are used as the sparsifying basis. In the initial work, we have shown that the algorithms perform well for images with sparse wavelets coefficients. In this work, we address the question of robustness and stability of the algorithms, specifically, if the image is not sparse and/or if noise is present. We show that our algorithms perform very well in the presence of a certain degree of noise. This is especially important for MRI and other real world applications where some level of noise is always present. 展开更多
关键词 Compressed sensing Reed-Muller SEQUENCES Chirps image RECONSTRUCTION
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Based on Compressed Sensing of Orthogonal Matching Pursuit Algorithm Image Recovery 被引量:4
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作者 Caifeng Cheng Deshu Lin 《Journal on Internet of Things》 2020年第1期37-45,共9页
Compressive sensing theory mainly includes the sparsely of signal processing,the structure of the measurement matrix and reconstruction algorithm.Reconstruction algorithm is the core content of CS theory,that is,throu... Compressive sensing theory mainly includes the sparsely of signal processing,the structure of the measurement matrix and reconstruction algorithm.Reconstruction algorithm is the core content of CS theory,that is,through the low dimensional sparse signal recovers the original signal accurately.This thesis based on the theory of CS to study further on seismic data reconstruction algorithm.We select orthogonal matching pursuit algorithm as a base reconstruction algorithm.Then do the specific research for the implementation principle,the structure of the algorithm of AOMP and make the signal simulation at the same time.In view of the OMP algorithm reconstruction speed is slow and the problems need to be a given number of iterations,which developed an improved scheme.We combine the optimized OMP algorithm of constraint the optimal matching of item selection strategy,the backwards gradient projection ideas of adaptive variance step gradient projection method and the original algorithm to improve it.Simulation experiments show that improved OMP algorithm is superior to traditional OMP algorithm of improvement in the reconstruction time and effect under the same condition.This paper introduces CS and most mature compressive sensing algorithm at present orthogonal matching pursuit algorithm.Through the program design realize basic orthogonal matching pursuit algorithms,and design realize basic orthogonal matching pursuit algorithm of one-dimensional,two-dimensional signal processing simulation. 展开更多
关键词 Compressed sensing sarse transform orthogonal matching pursuit image recovery
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