This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based ...This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.展开更多
BACKGROUND A key cardiac magnetic resonance(CMR)challenge is breath-holding duration,difficult for cardiac patients.AIM To evaluate whether artificial intelligence-assisted compressed sensing CINE(AICS-CINE)reduces im...BACKGROUND A key cardiac magnetic resonance(CMR)challenge is breath-holding duration,difficult for cardiac patients.AIM To evaluate whether artificial intelligence-assisted compressed sensing CINE(AICS-CINE)reduces image acquisition time of CMR compared to conventional CINE(C-CINE).METHODS Cardio-oncology patients(n=60)and healthy volunteers(n=29)underwent sequential C-CINE and AI-CS-CINE with a 1.5-T scanner.Acquisition time,visual image quality assessment,and biventricular metrics(end-diastolic volume,endsystolic volume,stroke volume,ejection fraction,left ventricular mass,and wall thickness)were analyzed and compared between C-CINE and AI-CS-CINE with Bland–Altman analysis,and calculation of intraclass coefficient(ICC).RESULTS In 89 participants(58.5±16.8 years,42 males,47 females),total AI-CS-CINE acquisition and reconstruction time(37 seconds)was 84%faster than C-CINE(238 seconds).C-CINE required repeats in 23%(20/89)of cases(approximately 8 minutes lost),while AI-CS-CINE only needed one repeat(1%;2 seconds lost).AICS-CINE had slightly lower contrast but preserved structural clarity.Bland-Altman plots and ICC(0.73≤r≤0.98)showed strong agreement for left ventricle(LV)and right ventricle(RV)metrics,including those in the cardiac amyloidosis subgroup(n=31).AI-CS-CINE enabled faster,easier imaging in patients with claustrophobia,dyspnea,arrhythmias,or restlessness.Motion-artifacted C-CINE images were reliably interpreted from AI-CS-CINE.CONCLUSION AI-CS-CINE accelerated CMR image acquisition and reconstruction,preserved anatomical detail,and diminished impact of patient-related motion.Quantitative AI-CS-CINE metrics agreed closely with C-CINE in cardio-oncology patients,including the cardiac amyloidosis cohort,as well as healthy volunteers regardless of left and right ventricular size and function.AI-CS-CINE significantly enhanced CMR workflow,particularly in challenging cases.The strong analytical concordance underscores reliability and robustness of AI-CS-CINE as a valuable tool.展开更多
This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specificall...This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specifically,this paper focuses on both channel modelling and receiver design for interference estimation and mitigation.We propose a delay-calibrated block-wise linear model,which extracts the delay of the dominant tap of each interference as a key parameter and approximates the residual channel coefficients by the recently developed blockwise linear model.Based on the delay-calibrated block-wise linear model and the angle-domain channel sparsity,we further conceive a message passing algorithm to solve the channel estimation problem.Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.展开更多
It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modu...It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.展开更多
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
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
We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement ti...We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.展开更多
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
The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will s...The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.展开更多
In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the Eur...In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.展开更多
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
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
Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,whi...Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.展开更多
Background:Contrast-enhanced magnetic resonance neurography(ceMRN)can enhance brachial plexus visualization and quality of imaging.However,the interval between contrast injection and scanning that provides the highest...Background:Contrast-enhanced magnetic resonance neurography(ceMRN)can enhance brachial plexus visualization and quality of imaging.However,the interval between contrast injection and scanning that provides the highest-quality images is not known.Methods:Fifteen patients underwent brachial plexus imaging using the 3D T2-NerveView sequence with a scanning duration of 5 min.A consecutive six-phase scan was initiated immediately at the start of contrast agent injection.Subsequently,all patients'images were classified into six groups according to the phases:group A(phase 1,delay 0 min),group B(phase 2,delay 5 min),group C(phase 3,delay 10 min),group D(phase 4,delay 15 min),group E(phase 5,delay 20 min),and group F(phase 6,delay 25 min).The image quality in each group was assessed based on nerve signal(signalnerve),muscle signal(signalmuscle),lymph node signal(signallymph node),background noise(BN),signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR),and subjective score.Results:Signalnerve,signalmuscle,BN,and SNR did not significantly differ among the six groups(p>0.05).However,significant differences(p<0.05)were observed in signallymph node(F=16.067),CNR(F=9.495),and subjective score(χ^(2)=23.586).As the scanning delay increased,signallymph node intensity gradually increased whereas the CNR gradually decreased.The subjective score was significantly higher in groups B(4.830.24),C(4.900.21),D(4.870.30),E(4.830.31),and F(4.830.31)than in group A(4.470.30).Conclusion:We recommend performing brachial plexus ceMRN 5 min after contrast injection.With this delay,the brachial plexus can be visualized optimally with minimal interference from background signals.展开更多
High-speed imaging is crucial for understanding the transient dynamics of the world,but conventional frame-by-frame video acquisition is limited by specialized hardware and substantial data storage requirements.We int...High-speed imaging is crucial for understanding the transient dynamics of the world,but conventional frame-by-frame video acquisition is limited by specialized hardware and substantial data storage requirements.We introduce“SpeedShot,”a computational imaging framework for efficient high-speed video imaging.SpeedShot features a low-speed dual-camera setup,which simultaneously captures two temporally coded snapshots.Cross-referencing these two snapshots extracts a multiplexed temporal gradient image,producing a compact and multiframe motion representation for video reconstruction.Recognizing the unique temporal-only modulation model,we propose an explicable motion-guided scale-recurrent transformer for video decoding.It exploits cross-scale error maps to bolster the cycle consistency between predicted and observed data.Evaluations on both simulated datasets and real imaging setups demonstrate SpeedShot’s effectiveness in video-rate up-conversion,with pronounced improvement over video frame interpolation and deblurring methods.The proposed framework is compatible with commercial low-speed cameras,offering a versatile low-bandwidth alternative for video-related applications,such as video surveillance and sports analysis.展开更多
In deep mineral exploration, the acquisition of audio magnetotelluric （AMT） data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical m...In deep mineral exploration, the acquisition of audio magnetotelluric （AMT） data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical morphological filtering （MMF） proved effective in suppressing large-scale strong and variably shaped noise, typically low-frequency noise, but can not deal with pulse noise of AMT data. We combine compressive sensing and MMF. First we use MMF to suppress the large-scale strong ambient noise; second, we use the improved orthogonal match pursuit （IOMP） algorithm to remove the residual pulse noise. To remove the noise and protect the useful AMT signal, a redundant dictionary that matches with spikes and is insensitive to the useful signal is designed. Synthetic and field data from the Luzong field suggest that the proposed method suppresses the near-source noise and preserves the signal well; thus, better results are obtained that improve the output of either MMF or IOMP.展开更多
A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing abil...A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.展开更多
High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar. After the factors which affect the sidelobe pedestal being analyzed, a compress...High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar. After the factors which affect the sidelobe pedestal being analyzed, a compressed sensing based algorithm for high resolution range imaging and a new minimized ll-norm criterion for motion compensation are proposed. The random hopping of the transmitted carrier frequency is converted to restricted isometry property of the observing matrix. Then practical problems of imaging model solution and signal parameter design are resolved. Due to the particularity of the proposed algorithm, two new indicators of range profile, i.e., average signal to sidelobe ratio and local similarity, are defined. The chamber measured data are adopted to testify the validity of the proposed algorithm, and simulations are performed to analyze the precision of velocity measurement as well as the performance of motion compensation. The simulation results show that the proposed algorithm has such advantages as high precision velocity measurement, low sidelobe and short period imaging, which ensure robust imaging for moving targets when signal-to-noise ratio is above 10 dB.展开更多
In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we p...In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we propose a sensing matrix optimization method in this paper,which considers the optimization under the guidance of the t%-averaged mutual coherence.First,we study sensing matrix optimization and model it as a constrained combinatorial optimization problem.Second,the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes,where the threshold t is derived through the K-means clustering.With the settled optimality index,a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search(GA-TLS)is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix.Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method,with much less localization error compared to the traditional sensing matrix optimization methods.展开更多
基金Supported by The Featured Innovation Projects of the General University of Guangdong Province(2023KTSCX096)The Special Projects in Key Areas of Guangdong Province(ZDZX1088)Research Team Project of Guangdong University of Education(2024KYCXTD018)。
文摘This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.
基金Supported by James Russell Hornsby and Jun Xiong Fund and United Imaging Healthcare.
文摘BACKGROUND A key cardiac magnetic resonance(CMR)challenge is breath-holding duration,difficult for cardiac patients.AIM To evaluate whether artificial intelligence-assisted compressed sensing CINE(AICS-CINE)reduces image acquisition time of CMR compared to conventional CINE(C-CINE).METHODS Cardio-oncology patients(n=60)and healthy volunteers(n=29)underwent sequential C-CINE and AI-CS-CINE with a 1.5-T scanner.Acquisition time,visual image quality assessment,and biventricular metrics(end-diastolic volume,endsystolic volume,stroke volume,ejection fraction,left ventricular mass,and wall thickness)were analyzed and compared between C-CINE and AI-CS-CINE with Bland–Altman analysis,and calculation of intraclass coefficient(ICC).RESULTS In 89 participants(58.5±16.8 years,42 males,47 females),total AI-CS-CINE acquisition and reconstruction time(37 seconds)was 84%faster than C-CINE(238 seconds).C-CINE required repeats in 23%(20/89)of cases(approximately 8 minutes lost),while AI-CS-CINE only needed one repeat(1%;2 seconds lost).AICS-CINE had slightly lower contrast but preserved structural clarity.Bland-Altman plots and ICC(0.73≤r≤0.98)showed strong agreement for left ventricle(LV)and right ventricle(RV)metrics,including those in the cardiac amyloidosis subgroup(n=31).AI-CS-CINE enabled faster,easier imaging in patients with claustrophobia,dyspnea,arrhythmias,or restlessness.Motion-artifacted C-CINE images were reliably interpreted from AI-CS-CINE.CONCLUSION AI-CS-CINE accelerated CMR image acquisition and reconstruction,preserved anatomical detail,and diminished impact of patient-related motion.Quantitative AI-CS-CINE metrics agreed closely with C-CINE in cardio-oncology patients,including the cardiac amyloidosis cohort,as well as healthy volunteers regardless of left and right ventricular size and function.AI-CS-CINE significantly enhanced CMR workflow,particularly in challenging cases.The strong analytical concordance underscores reliability and robustness of AI-CS-CINE as a valuable tool.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800。
文摘This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specifically,this paper focuses on both channel modelling and receiver design for interference estimation and mitigation.We propose a delay-calibrated block-wise linear model,which extracts the delay of the dominant tap of each interference as a key parameter and approximates the residual channel coefficients by the recently developed blockwise linear model.Based on the delay-calibrated block-wise linear model and the angle-domain channel sparsity,we further conceive a message passing algorithm to solve the channel estimation problem.Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.
基金supported by the National Natural Science Foundation of China(61931012,62171258,62088102,and 62271414)the Zhejiang Provincial Outstanding Youth Science Foundation(LR23F010001)the Key Project of Westlake Institute for Optoelectronics(2023GD007).
文摘It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 71571091,71771112the State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds under Grant PAL-N201801the Excellent Talent Training Project of University of Science and Technology Liaoning under Grant 2019RC05.
文摘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.
基金Project supported by the Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2020MF119 and ZR2020MA082)the National Natural Science Foundation of China(Grant No.62002208)the National Key Research and Development Program of China(Grant No.2018YFB0504302).
文摘We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.
基金the National Natural Science Foundation of China(Nos.62002028,62102040 and 62202066).
文摘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.
基金The National Natural Science Foundation of China(No.11274259)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education(No.UASP1305)
文摘The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.
基金The National Natural Science Foundation of China (No.60872075)the National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)
文摘In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.
文摘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.
基金The National Natural Science Foundation of China(No.51575256)the Fundamental Research Funds for the Central Universities(No.NP2015101,XZA16003)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘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.
基金supported by the National Natural Science Foundation of China(62001481,61890542,62071475)the Natural Science Foundation of Hunan Province(2022JJ40561)the Research Program of National University of Defense Technology(ZK22-46).
文摘Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.
基金supported by the National Natural Science Foundation of China(Grant No.82302173).
文摘Background:Contrast-enhanced magnetic resonance neurography(ceMRN)can enhance brachial plexus visualization and quality of imaging.However,the interval between contrast injection and scanning that provides the highest-quality images is not known.Methods:Fifteen patients underwent brachial plexus imaging using the 3D T2-NerveView sequence with a scanning duration of 5 min.A consecutive six-phase scan was initiated immediately at the start of contrast agent injection.Subsequently,all patients'images were classified into six groups according to the phases:group A(phase 1,delay 0 min),group B(phase 2,delay 5 min),group C(phase 3,delay 10 min),group D(phase 4,delay 15 min),group E(phase 5,delay 20 min),and group F(phase 6,delay 25 min).The image quality in each group was assessed based on nerve signal(signalnerve),muscle signal(signalmuscle),lymph node signal(signallymph node),background noise(BN),signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR),and subjective score.Results:Signalnerve,signalmuscle,BN,and SNR did not significantly differ among the six groups(p>0.05).However,significant differences(p<0.05)were observed in signallymph node(F=16.067),CNR(F=9.495),and subjective score(χ^(2)=23.586).As the scanning delay increased,signallymph node intensity gradually increased whereas the CNR gradually decreased.The subjective score was significantly higher in groups B(4.830.24),C(4.900.21),D(4.870.30),E(4.830.31),and F(4.830.31)than in group A(4.470.30).Conclusion:We recommend performing brachial plexus ceMRN 5 min after contrast injection.With this delay,the brachial plexus can be visualized optimally with minimal interference from background signals.
基金supported by the National Natural Science Foundation of China(Grant No.62305184)the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)+7 种基金the Science,Technology,and Innovation Commission of Shenzhen Municipality(Grant No.JCYJ20241202123919027)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)the Science Fund for Distinguished Young Scholars of Zhejiang Province(Grant No.LR23F010001)the Research Center for Industries of the Future(RCIF)at Westlake University and and the Key Project of Westlake Institute for Optoelectronics(Grant No.2023GD007)the Zhejiang“Pioneer”and“Leading Goose”R&D Program(Grant Nos.2024SDXHDX0006 and 2024C03182)the Ningbo Science and Technology Bureau“Science and Technology Yongjiang 2035”Key Technology Breakthrough Program(Grant No.2024Z126)the Research Grants Council of the Hong Kong Special Administrative Region,China(Grant Nos.C5031-22G,CityU11310522,and CityU11300123)the City University of Hong Kong(Grant No.9610628).
文摘High-speed imaging is crucial for understanding the transient dynamics of the world,but conventional frame-by-frame video acquisition is limited by specialized hardware and substantial data storage requirements.We introduce“SpeedShot,”a computational imaging framework for efficient high-speed video imaging.SpeedShot features a low-speed dual-camera setup,which simultaneously captures two temporally coded snapshots.Cross-referencing these two snapshots extracts a multiplexed temporal gradient image,producing a compact and multiframe motion representation for video reconstruction.Recognizing the unique temporal-only modulation model,we propose an explicable motion-guided scale-recurrent transformer for video decoding.It exploits cross-scale error maps to bolster the cycle consistency between predicted and observed data.Evaluations on both simulated datasets and real imaging setups demonstrate SpeedShot’s effectiveness in video-rate up-conversion,with pronounced improvement over video frame interpolation and deblurring methods.The proposed framework is compatible with commercial low-speed cameras,offering a versatile low-bandwidth alternative for video-related applications,such as video surveillance and sports analysis.
基金supported by the National High Technology Research and Development Program of China(No.2014AA06A602)National Natural Science Foundation of China(No.41404111)Natural Science Foundation of Hunan Province(No.2015JJ3088)
文摘In deep mineral exploration, the acquisition of audio magnetotelluric （AMT） data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical morphological filtering （MMF） proved effective in suppressing large-scale strong and variably shaped noise, typically low-frequency noise, but can not deal with pulse noise of AMT data. We combine compressive sensing and MMF. First we use MMF to suppress the large-scale strong ambient noise; second, we use the improved orthogonal match pursuit （IOMP） algorithm to remove the residual pulse noise. To remove the noise and protect the useful AMT signal, a redundant dictionary that matches with spikes and is insensitive to the useful signal is designed. Synthetic and field data from the Luzong field suggest that the proposed method suppresses the near-source noise and preserves the signal well; thus, better results are obtained that improve the output of either MMF or IOMP.
基金The National Basic Research Program of China(973Program)(No.2011CB707904)the National Natural Science Foundation of China(No.61201344,61271312,61073138)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092110023,20120092120036)the Natural Science Foundation of Jiangsu Province(No.BK2012329)
文摘A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.
基金Project(61171133) supported by the National Natural Science Foundation of ChinaProject(CX2011B019) supported by Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(B110404) supported by Innovation Foundation for Outstanding Postgraduates of National University of Defense Technology,China
文摘High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar. After the factors which affect the sidelobe pedestal being analyzed, a compressed sensing based algorithm for high resolution range imaging and a new minimized ll-norm criterion for motion compensation are proposed. The random hopping of the transmitted carrier frequency is converted to restricted isometry property of the observing matrix. Then practical problems of imaging model solution and signal parameter design are resolved. Due to the particularity of the proposed algorithm, two new indicators of range profile, i.e., average signal to sidelobe ratio and local similarity, are defined. The chamber measured data are adopted to testify the validity of the proposed algorithm, and simulations are performed to analyze the precision of velocity measurement as well as the performance of motion compensation. The simulation results show that the proposed algorithm has such advantages as high precision velocity measurement, low sidelobe and short period imaging, which ensure robust imaging for moving targets when signal-to-noise ratio is above 10 dB.
基金supported by the National Natural Science Foundation of China(6107116361071164+5 种基金6147119161501233)the Fundamental Research Funds for the Central Universities(NP2014504)the Aeronautical Science Foundation(20152052026)the Electronic & Information School of Yangtze University Innovation Foundation(2016-DXCX-05)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we propose a sensing matrix optimization method in this paper,which considers the optimization under the guidance of the t%-averaged mutual coherence.First,we study sensing matrix optimization and model it as a constrained combinatorial optimization problem.Second,the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes,where the threshold t is derived through the K-means clustering.With the settled optimality index,a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search(GA-TLS)is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix.Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method,with much less localization error compared to the traditional sensing matrix optimization methods.
