Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
Incoherent digital holography has attracted significant attention due to its advantages in threedimensional(3D)imaging under low spatial coherence conditions,such as easy access to light sources and reduced speckle no...Incoherent digital holography has attracted significant attention due to its advantages in threedimensional(3D)imaging under low spatial coherence conditions,such as easy access to light sources and reduced speckle noise.However,interlayer crosstalk during the reconstruction process leads to a substantial reduction in reconstruction fidelity.Furthermore,existing deconvolutionand deep-learning-based reconstruction algorithms face limitations in terms of effectiveness and generalization.To address these challenges,we propose a compressive incoherent digital holography(CIDH)approach for 3D imaging.In CIDH,a point spread hologram sequence with a high signal-to-noise ratio is initially obtained using a customized computergenerated holography method for dual-channel forward data acquisition.For scene reconstruction,a compressed sensing-based two-step iterative shrinkage/thresholding algorithm is employed to achieve high-fidelity 3D scene retrieval.The combined optimization demonstrates exceptional performance in suppressing interlayer crosstalk and enhancing reconstruction fidelity.In simulations,crosstalk was effectively suppressed across 10 depth layers.In experiments,successful suppression was achieved for both a five-layer transmissive object and a two-layer reflective 3D object,resulting in significantly improved reconstruction accuracy.The proposed framework shows great potential for applications in various incoherent source-illuminated and fluorescent 3D imaging.展开更多
With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communica...With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communication.Multi-view image compression aims to improve compression efficiency by leveraging correlations between images.However,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained devices.In this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding.Our model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image reconstruction.The encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy coding.The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side.Specifically,we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps.Second,we employ a semi-coupled convolution module to extract both shared and specific information in images.Finally,a cross-attention module is employed to fuse mutual information extracted from side information.The effectiveness of our model is validated on various datasets,including KITTI Stereo and Cityscapes.The results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.展开更多
Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by ...Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.展开更多
BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and H...BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.展开更多
Coded excitation is useful for ultrasound contrast imaging to increase penetration and SNR, and improve the contrast to tissue ratio (CTR). The waveform of bubble response depends greatly on bubble size, the frequency...Coded excitation is useful for ultrasound contrast imaging to increase penetration and SNR, and improve the contrast to tissue ratio (CTR). The waveform of bubble response depends greatly on bubble size, the frequency and bandwidth of the excitation chirp signal. This makes the pulse compression filter based on square-law be wrong for bubbles with changing sizes. In this paper, an adaptive pulse compression (APC) filter for the second harmonic of microbubble with varying size distribution is proposed. The APC filter is designed based on the estimated power spectrum of the received bubble harmonic echoes. Theoretical analysis and simulation studies are presented for evaluating performance of the APC filter. For monodisperse bubble, the power improvement factor of the APC filter can be more than 20 dB.展开更多
[Objective] To study the digital image compression technology in rice monitoring system. [Method] A digital image compression technology program based on the discrete Fourier transform was proposed, and simulation exp...[Objective] To study the digital image compression technology in rice monitoring system. [Method] A digital image compression technology program based on the discrete Fourier transform was proposed, and simulation experiments were carried out to compress the image at different compression ratios. [Result] When com- pression ratios were less than 30, the compression ratio, image entropy, average codeword length, coding efficiency and redundancy which reflected the quality of the coding, and the parameter PSNR which estimated the fidelity of the compressed im- age were all achieved good results that human eye could barely percept the differ- ence between the original image and decompressed image; and when the compres- sion ratios were more than 30, there was a certain distortion in the decompressed image. And when the compression ratio was 91.516 3, although the image had some distortion, the PSNR was still achieved to 21.528 2, and human eye could accept the decompressed image intuitively within the acceptable error range. [Conclusion] The results show that the proposed image compression program is a viable, effective, and better image compression technology which can satisfy the requirements of the crop monitoring system on image storage, transforming and transporting.展开更多
[Objective] The aim was to present a proposal about a new image compression technology, in order to make the image be able to be stored in a smaller space and be transmitted with smaller bit rate on the premise of gua...[Objective] The aim was to present a proposal about a new image compression technology, in order to make the image be able to be stored in a smaller space and be transmitted with smaller bit rate on the premise of guaranteeing image quality in the rape crop monitoring system in Qinling Mountains. [Method] In the proposal, the color image was divided into brightness images with three fundamental colors, followed by sub-image division and DCT treatment. Then, coefficients of transform domain were quantized, and encoded and compressed as per Huffman coding. Finally, decompression was conducted through inverse process and decompressed images were matched. [Result] The simulation results show that when compression ratio of the color image of rape crops was 11.972 3∶1, human can not distinguish the differences between the decompressed images and the source images with naked eyes; when ratio was as high as 53.565 6∶1, PSNR was still above 30 dD,encoding efficiency achieved over 0.78 and redundancy was less than 0.22. [Conclusion] The results indicate that the proposed color image compression technology can achieve higher compression ratio on the premise of good image quality. In addition, image encoding quality and decompressed images achieved better results, which fully met requirement of image storage and transmission in monitoring system of rape crop in the Qinling Mountains.展开更多
Objective:To describe the anatomical characteristics and patterns of neurovascular compression (NVC) in patients suffering trigeminal neuralgia(TN) by 3D high-resolution magnetic resonance imaging(MRI) method and imag...Objective:To describe the anatomical characteristics and patterns of neurovascular compression (NVC) in patients suffering trigeminal neuralgia(TN) by 3D high-resolution magnetic resonance imaging(MRI) method and image fusion technique.Methods:The anatomic structure of trigeminal nerve,brain stem and blood vessel was observed in 100 consecutive TN patients by 3D high resolution MRI(3D SPGR,contrast-enhanced T1 3D MP-RAGE and T2/T1 3D FIESTA). The 3D image sources were fused and visualized using 3D DOCTOR software.Results:One or several NVC sites,which usually appeared 0-9.8 mm away from brain stem,were found on the symptomatic side in 93%of the TN cases.Superior cerebellar artery was involved in 76%(71/93) of these cases.The other vessels including antero-inferior cerebellar artery,vertebral artery, basilar artery and veins also contributed to the occurrence of NVC.The NVC sites were found to be located in the proximal segment in 42%of these cases(39/93) and in the distal segment in 45% (42/93).Nerve dislocation or distortion was observed in 32%(30/93).Conclusions:Various 3D high resolution MRI methods combined with the image fusion technique could provide pathologic anatomic information for the diagnosis and treatment of TN.展开更多
The complete stress-strain characteristics of sandstone specimens were investigated in a series of quasistatic monotonic uniaxial compression tests.Strain patterns development during pre-and post-peak behaviours in sp...The complete stress-strain characteristics of sandstone specimens were investigated in a series of quasistatic monotonic uniaxial compression tests.Strain patterns development during pre-and post-peak behaviours in specimens with different aspect ratios was also examined.Peak stress,post-peak portion of stress-strain,brittleness,characteristics of progressive localisation and field strain patterns development were affected at different extents by specimen aspect ratio.Strain patterns of the rocks were obtained by applying three-dimensional(3D) digital image correlation(DIC) technique.Unlike conventional strain measurement using strain gauges attached to specimen,3D DIC allowed not only measuring large strains,but more importantly,mapping the development of field strain throughout the compression test,i.e.in pre-and post-peak regimes.Field strain development in the surface of rock specimen suggests that strain starts localising progressively and develops at a lower rate in pre-peak regime.However,in post-peak regime,strains increase at different rates as local deformations take place at different extents in the vicinity and outside the localised zone.The extent of localised strains together with the rate of strain localisation is associated with the increase in rate of strength degradation.Strain localisation and local inelastic unloading outside the localised zone both feature post-peak regime.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
We propose a compressed ghost imaging scheme based on differential speckle patterns,named CGI-DSP.In the scheme,a series of bucket detector signals are acquired when a series of random speckle patterns are employed to...We propose a compressed ghost imaging scheme based on differential speckle patterns,named CGI-DSP.In the scheme,a series of bucket detector signals are acquired when a series of random speckle patterns are employed to illuminate an unknown object.Then the differential speckle patterns(differential bucket detector signals)are obtained by taking the difference between present random speckle patterns(present bucket detector signals)and previous random speckle patterns(previous bucket detector signals).Finally,the image of object can be obtained directly by performing the compressed sensing algorithm on the differential speckle patterns and differential bucket detector signals.The experimental and simulated results reveal that CGI-DSP can improve the imaging quality and reduce the number of measurements comparing with the traditional compressed ghost imaging schemes because our scheme can remove the environmental illuminations efficiently.展开更多
Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability...Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.展开更多
The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target refle...The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.展开更多
As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algor...As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.展开更多
A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. ...A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. Then, the best-matched one for every range block is obtained by analysing the relation between their coefficients. Experimental results show that the proposed method can shorten encoding time markedly, while the retrieved image quality is still acceptable. In the decoding step, a kind of simple line fitting on block boundaries is used to reduce blocking effects. At the same time, the proposed method can also achieve a high compression ratio.展开更多
This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture f...This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.展开更多
By investigating the limitation of existing wavelet tree based image compression methods, we propose a novel wavelet fractal image compression method in this paper. Briefly, the initial errors are appointed given the ...By investigating the limitation of existing wavelet tree based image compression methods, we propose a novel wavelet fractal image compression method in this paper. Briefly, the initial errors are appointed given the different levels of importance accorded the frequency sublevel band wavelet coefficients. Higher frequency sublevel bands would lead to larger initial errors. As a result, the sizes of sublevel blocks and super blocks would be changed according to the initial errors. The matching sizes between sublevel blocks and super blocks would be changed according to the permitted errors and compression rates. Systematic analyses are performed and the experimental results demonstrate that the proposed method provides a satisfactory performance with a clearly increasing rate of compression and speed of encoding without reducing SNR and the quality of decoded images. Simulation results show that our method is superior to the traditional wavelet tree based methods of fractal image compression.展开更多
A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, i...A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.展开更多
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
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
基金supported by the National Natural Science Foundation of China(Grant Nos.12325408,12274129,12374274,12274139,62175066,92150102,62475070,12474404,12471368,and 12304338)the Shanghai Municipal Education Commission(Grant No.2024AI01007).
文摘Incoherent digital holography has attracted significant attention due to its advantages in threedimensional(3D)imaging under low spatial coherence conditions,such as easy access to light sources and reduced speckle noise.However,interlayer crosstalk during the reconstruction process leads to a substantial reduction in reconstruction fidelity.Furthermore,existing deconvolutionand deep-learning-based reconstruction algorithms face limitations in terms of effectiveness and generalization.To address these challenges,we propose a compressive incoherent digital holography(CIDH)approach for 3D imaging.In CIDH,a point spread hologram sequence with a high signal-to-noise ratio is initially obtained using a customized computergenerated holography method for dual-channel forward data acquisition.For scene reconstruction,a compressed sensing-based two-step iterative shrinkage/thresholding algorithm is employed to achieve high-fidelity 3D scene retrieval.The combined optimization demonstrates exceptional performance in suppressing interlayer crosstalk and enhancing reconstruction fidelity.In simulations,crosstalk was effectively suppressed across 10 depth layers.In experiments,successful suppression was achieved for both a five-layer transmissive object and a two-layer reflective 3D object,resulting in significantly improved reconstruction accuracy.The proposed framework shows great potential for applications in various incoherent source-illuminated and fluorescent 3D imaging.
基金supported by the National Natural Science Foundation of China(Key Program)(No.11932013)the Tianjin Science and Technology Plan Project(No.22PTZWHZ00040)。
文摘With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data communication.Multi-view image compression aims to improve compression efficiency by leveraging correlations between images.However,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained devices.In this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding.Our model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image reconstruction.The encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy coding.The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side.Specifically,we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps.Second,we employ a semi-coupled convolution module to extract both shared and specific information in images.Finally,a cross-attention module is employed to fuse mutual information extracted from side information.The effectiveness of our model is validated on various datasets,including KITTI Stereo and Cityscapes.The results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.62031013)Guangdong Province Key Construction Discipline Scientific Research Capacity Improvement Project(Grant No.2022ZDJS117).
文摘Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.
基金Supported by the Key Research and Development Plan of Shaanxi Province,No.2021SF-298.
文摘BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.
文摘Coded excitation is useful for ultrasound contrast imaging to increase penetration and SNR, and improve the contrast to tissue ratio (CTR). The waveform of bubble response depends greatly on bubble size, the frequency and bandwidth of the excitation chirp signal. This makes the pulse compression filter based on square-law be wrong for bubbles with changing sizes. In this paper, an adaptive pulse compression (APC) filter for the second harmonic of microbubble with varying size distribution is proposed. The APC filter is designed based on the estimated power spectrum of the received bubble harmonic echoes. Theoretical analysis and simulation studies are presented for evaluating performance of the APC filter. For monodisperse bubble, the power improvement factor of the APC filter can be more than 20 dB.
基金Supported by the Natural Science Foundation of Shaanxi Province,China (2011JE012)the Special Research Fund of the Education Bureau of Shaanxi Province,China(2010JK464)~~
文摘[Objective] To study the digital image compression technology in rice monitoring system. [Method] A digital image compression technology program based on the discrete Fourier transform was proposed, and simulation experiments were carried out to compress the image at different compression ratios. [Result] When com- pression ratios were less than 30, the compression ratio, image entropy, average codeword length, coding efficiency and redundancy which reflected the quality of the coding, and the parameter PSNR which estimated the fidelity of the compressed im- age were all achieved good results that human eye could barely percept the differ- ence between the original image and decompressed image; and when the compres- sion ratios were more than 30, there was a certain distortion in the decompressed image. And when the compression ratio was 91.516 3, although the image had some distortion, the PSNR was still achieved to 21.528 2, and human eye could accept the decompressed image intuitively within the acceptable error range. [Conclusion] The results show that the proposed image compression program is a viable, effective, and better image compression technology which can satisfy the requirements of the crop monitoring system on image storage, transforming and transporting.
基金Supported by Special Fund for Scientific Research of Shannxi Education Department(No:2010JK463)Shaanxi Natural Science Foundation(2011JE012)~~
文摘[Objective] The aim was to present a proposal about a new image compression technology, in order to make the image be able to be stored in a smaller space and be transmitted with smaller bit rate on the premise of guaranteeing image quality in the rape crop monitoring system in Qinling Mountains. [Method] In the proposal, the color image was divided into brightness images with three fundamental colors, followed by sub-image division and DCT treatment. Then, coefficients of transform domain were quantized, and encoded and compressed as per Huffman coding. Finally, decompression was conducted through inverse process and decompressed images were matched. [Result] The simulation results show that when compression ratio of the color image of rape crops was 11.972 3∶1, human can not distinguish the differences between the decompressed images and the source images with naked eyes; when ratio was as high as 53.565 6∶1, PSNR was still above 30 dD,encoding efficiency achieved over 0.78 and redundancy was less than 0.22. [Conclusion] The results indicate that the proposed color image compression technology can achieve higher compression ratio on the premise of good image quality. In addition, image encoding quality and decompressed images achieved better results, which fully met requirement of image storage and transmission in monitoring system of rape crop in the Qinling Mountains.
基金Supported by the Science Foundation of Haikou Health Bureau (grant No.2010-SWY-13-058)Haikou Science Technology Information Bureau (grant No.2009-049-1)
文摘Objective:To describe the anatomical characteristics and patterns of neurovascular compression (NVC) in patients suffering trigeminal neuralgia(TN) by 3D high-resolution magnetic resonance imaging(MRI) method and image fusion technique.Methods:The anatomic structure of trigeminal nerve,brain stem and blood vessel was observed in 100 consecutive TN patients by 3D high resolution MRI(3D SPGR,contrast-enhanced T1 3D MP-RAGE and T2/T1 3D FIESTA). The 3D image sources were fused and visualized using 3D DOCTOR software.Results:One or several NVC sites,which usually appeared 0-9.8 mm away from brain stem,were found on the symptomatic side in 93%of the TN cases.Superior cerebellar artery was involved in 76%(71/93) of these cases.The other vessels including antero-inferior cerebellar artery,vertebral artery, basilar artery and veins also contributed to the occurrence of NVC.The NVC sites were found to be located in the proximal segment in 42%of these cases(39/93) and in the distal segment in 45% (42/93).Nerve dislocation or distortion was observed in 32%(30/93).Conclusions:Various 3D high resolution MRI methods combined with the image fusion technique could provide pathologic anatomic information for the diagnosis and treatment of TN.
基金supported by the Deep Exploration Technologies Cooperative Research Centre whose activities are funded by the Australian Government's Cooperative Research Centre Programme.This is DET CRC Document 2017/954
文摘The complete stress-strain characteristics of sandstone specimens were investigated in a series of quasistatic monotonic uniaxial compression tests.Strain patterns development during pre-and post-peak behaviours in specimens with different aspect ratios was also examined.Peak stress,post-peak portion of stress-strain,brittleness,characteristics of progressive localisation and field strain patterns development were affected at different extents by specimen aspect ratio.Strain patterns of the rocks were obtained by applying three-dimensional(3D) digital image correlation(DIC) technique.Unlike conventional strain measurement using strain gauges attached to specimen,3D DIC allowed not only measuring large strains,but more importantly,mapping the development of field strain throughout the compression test,i.e.in pre-and post-peak regimes.Field strain development in the surface of rock specimen suggests that strain starts localising progressively and develops at a lower rate in pre-peak regime.However,in post-peak regime,strains increase at different rates as local deformations take place at different extents in the vicinity and outside the localised zone.The extent of localised strains together with the rate of strain localisation is associated with the increase in rate of strength degradation.Strain localisation and local inelastic unloading outside the localised zone both feature post-peak regime.
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 supported by the National Natural Science Foundation of China(Grant Nos.11847062 and 61871234)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20180755)the Science Fund from NUPT(Grant No.NY218098).
文摘We propose a compressed ghost imaging scheme based on differential speckle patterns,named CGI-DSP.In the scheme,a series of bucket detector signals are acquired when a series of random speckle patterns are employed to illuminate an unknown object.Then the differential speckle patterns(differential bucket detector signals)are obtained by taking the difference between present random speckle patterns(present bucket detector signals)and previous random speckle patterns(previous bucket detector signals).Finally,the image of object can be obtained directly by performing the compressed sensing algorithm on the differential speckle patterns and differential bucket detector signals.The experimental and simulated results reveal that CGI-DSP can improve the imaging quality and reduce the number of measurements comparing with the traditional compressed ghost imaging schemes because our scheme can remove the environmental illuminations efficiently.
文摘Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.
基金supported by the Fundamental Research Funds for the Central Universities(DL13BB21)the Natural Science Foundation of Heilongjiang Province(C2015054)+1 种基金Heilongjiang Province Technology Foundation for Selected Osverseas ChineseNatural Science Foundation of Heilongjiang Province(F2015036)
文摘As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61173183, 60973152, and 60573172)the Special Scientific Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20070141014)the Natural Science Foundation of Liaoning Province, China (Grant No. 20082165)
文摘A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. Then, the best-matched one for every range block is obtained by analysing the relation between their coefficients. Experimental results show that the proposed method can shorten encoding time markedly, while the retrieved image quality is still acceptable. In the decoding step, a kind of simple line fitting on block boundaries is used to reduce blocking effects. At the same time, the proposed method can also achieve a high compression ratio.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60573172 and 60973152)the Superior University Doctor Subject Special Scientific Research Foundation of China (Grant No. 20070141014)the Natural Science Foundation of Liaoning Province of China (Grant No. 20082165)
文摘This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.
基金Project 60571049 supported by the National Natural Science Foundation of China
文摘By investigating the limitation of existing wavelet tree based image compression methods, we propose a novel wavelet fractal image compression method in this paper. Briefly, the initial errors are appointed given the different levels of importance accorded the frequency sublevel band wavelet coefficients. Higher frequency sublevel bands would lead to larger initial errors. As a result, the sizes of sublevel blocks and super blocks would be changed according to the initial errors. The matching sizes between sublevel blocks and super blocks would be changed according to the permitted errors and compression rates. Systematic analyses are performed and the experimental results demonstrate that the proposed method provides a satisfactory performance with a clearly increasing rate of compression and speed of encoding without reducing SNR and the quality of decoded images. Simulation results show that our method is superior to the traditional wavelet tree based methods of fractal image compression.
基金This project was supported by the National Natural Science Foundation of China (60532060)Hainan Education Bureau Research Project (Hjkj200602)Hainan Natural Science Foundation (80551).
文摘A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.
基金Project supported by the National Natural Science Foundation of China(Grant No.61271238)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20123223110003)the University Natural Science Research Foundation of Jiangsu Province,China(Grant No.11KJA510002)
文摘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.
