Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagn...Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.展开更多
Full matrix focusing method of ultrasonic phased array has been proved with advantages of good signal-to-noise ratio and imaging resolution in the field of Ultrasonic NDT.However,it is still suffering from the time-co...Full matrix focusing method of ultrasonic phased array has been proved with advantages of good signal-to-noise ratio and imaging resolution in the field of Ultrasonic NDT.However,it is still suffering from the time-consuming data acquisition and processing.In order to solve the problem,two simplified matrix focusing methods are provided in the paper.One provided method is a triangular matrix focusing algorithm based on the principle of reciprocity for the multi-channel ultrasonic system.The other provided method is a trapezoidal matrix focusing algorithm based on the energy weight of the different channel to the focusing area.Time of data acquisition and computational is decreased with the provided simplified matrix focusing methods.In order to prove the validity of two provided algorithms,both side-drilled holes and oblique cracks are used for imaging experiments.The experimental results show that the imaging quality of the triangular matrix focusing algorithm is basically consistent to that of the full matrix focusing method.And imaging quality of the trapezoidal matrix focusing algorithm is slightly reduced with the amount of multi-channel data decreasing.Both data acquisition and computational efficiency using the triangular matrix focusing algorithm and the trapezoidal matrix focusing algorithm have been improved significantly compared with original full matrix focusing method.展开更多
Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imag...Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.展开更多
Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One w...Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.展开更多
A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can ...A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can hardly be seen in the results obtained by traditional imaging algorithm.To solve this problem,we propose a millimeter-wave LFMCW radar imaging algorithm for water surface texture.Different from the traditional imaging algorithm,the proposed imaging algorithm includes two improvements as follows:Firstly,the interference from static targets is removed through a frequency domainfilter;Secondly,the multiplicative noises are reduced by the maximum likelihood estimation method,which is used to estimatethe azimuth spectrum parameters to calculate the energy of water surface echo.Final results show that the proposed algorithmcan obtain water surface texture,which means that the proposed algorithm is superior to the traditional imaging algorithm.展开更多
Hepatocellular carcinoma(HCC)is a leading cause of morbidity and mortality worldwide,with rising clinical and economic burden as incidence increases.There are a multitude of evolving treatment options,including locore...Hepatocellular carcinoma(HCC)is a leading cause of morbidity and mortality worldwide,with rising clinical and economic burden as incidence increases.There are a multitude of evolving treatment options,including locoregional therapies which can be used alone,in combination with each other,or in combination with systemic therapy.These treatment options have shown to be effective in achieving remission,controlling tumor progression,improving disease free and overall survival in patients who cannot undergo resection and providing a bridge to transplant by debulking tumor burden to downstage patients.Following locoregional therapy(LRT),it is crucial to provide treatment response assessment to guide management and liver transplant candidacy.Therefore,Liver Imaging Reporting and Data Systems(LI-RADS)Treatment Response Algorithm(TRA)was created to provide a standardized assessment of HCC following LRT.LIRADS TRA provides a step by step approach to evaluate each lesion independently for accurate tumor assessment.In this review,we provide an overview of different locoregional therapies for HCC,describe the expected post treatment imaging appearance following treatment,and review the LI-RADS TRA with guidance for its application in clinical practice.Unique to other publications,we will also review emerging literature supporting the use of LI-RADS for assessment of HCC treatment response after LRT.展开更多
In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF...In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise diagnosis.To address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into consideration.Using the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion enhancement.Pathological consistency loss is also applied to maintain fundus feature integrity,significantly improving image quality.Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after enhancement.In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease classification.The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.展开更多
This paper focuses on the general case （GC） airborne bistatic synthetic aperture radar （SAR） data processing, and a new analytical imaging algorithm based on the extended Loffeld＇s bistatic formula （ELBF） is pr...This paper focuses on the general case （GC） airborne bistatic synthetic aperture radar （SAR） data processing, and a new analytical imaging algorithm based on the extended Loffeld＇s bistatic formula （ELBF） is proposed. According to the bistatic SAR geometry, the track decoupling formulas that convert the bistatic geometry to the receiver-referenced geometry in a concise way are derived firstly. Then phase terms of ELBF are decomposed into two independent phase terms as the range phase term and the azimuth phase term in a new way. To get the focusing result, the bistatic deformation （BD） term is compensated in the two-dimensional （2- D） frequency domain, and the space-variances of the range phase term and the azimuth phase term are eliminated by chirp scaling （CS） and chirp z-transform （CZT）, respectively. The effectiveness of the proposed algorithm is verified by the simulation results.展开更多
In the moment-ratio imaging algorithm, which is based on the theory of healing of a wound, the energy of each strong earthquake is distributed around the epicenter according to certain rules, and the features of the M...In the moment-ratio imaging algorithm, which is based on the theory of healing of a wound, the energy of each strong earthquake is distributed around the epicenter according to certain rules, and the features of the Moment-ratio value R are analyzed as the space and time change, so that the relationships between the moment-ration value R and strong earthquakes can be found. In the present paper, regions divided, hypocenter depths and events completed magnitude analyses were carried out in the Chinese catalogue. By applying the moment-ratio imaging algorithm in which the parameters are adjusted, the processes of anomaly evolution which correspond to the epicenter and the surrounding value R before earthquakes of M≥7.0 since 1966 in different areas of China were analyzed. It was found that the range area and imminent time of a coming earthquake could be confirmed quantita- tively by analyzing the abnormal temporal and spatial variation of the value R The results showed that the temporal and spatial variation of the value R could quantitatively reflect the temporal and spatial factors of a coming strong earthquake as well as the rule of medium rupture.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevale...This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.展开更多
In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quan...In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform(QAr T) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QAr T to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of “Monkey”, “Flower”, and “House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate(NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity(UACI) is 33.41%,and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.展开更多
Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects.Event cameras,as high temporal resolution bionic cameras,record intensity changes in an asynchronous manner,and their record...Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects.Event cameras,as high temporal resolution bionic cameras,record intensity changes in an asynchronous manner,and their recorded high temporal resolution information can effectively solve the problem of time information loss in motion blur.Existing event-based deblurring methods still face challenges when facing high-speed moving objects.We conducted an in-depth study of the imaging principle of event cameras.We found that the event stream contains excessive noise.The valid information is sparse.Invalid event features hinder the expression of valid features due to the uncertainty of the global threshold.To address this problem,a denoising-based long and short-term memory module(DTM)is designed in this paper.The DTM suppressed the original event information by noise reduction process.Invalid features in the event stream and solves the problem of sparse valid information in the event stream,and it also combines with the long short-term memory module(LSTM),which further enhances the event feature information in the time scale.In addition,through the in-depth understanding of the unique characteristics of event features,it is found that the high-frequency information recorded by event features does not effectively guide the fusion feature deblurring process in the spatial-domain-based feature processing,and for this reason,we introduce the residual fast fourier transform module(RES-FFT)to further enhance the high-frequency characteristics of the fusion features by performing the feature extraction of the fusion features from the perspective of the frequency domain.Ultimately,our proposed event image fusion network based on event denoising and frequency domain feature enhancement(DNEFNET)achieved Peak Signal-to-Noise Ratio(PSNR)/Structural Similarity Index Measure(SSIM)scores of 35.55/0.972 on the GoPro dataset and 38.27/0.975 on the REBlur dataset,achieving the state of the art(SOTA)effect.展开更多
In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the qualit...In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the quality information. Abundant weld quality information is contained in weld pool and keyhole. Aiming at Nd：YAG laser welding of stainless steel, a coaxial visual sensing system was constructed. The images of weld pool and keyhole were obtained. Based on the gray character of weld pool and keyhole in images, an image processing algorithm was designed. The search start point and search criteria of weld pool and keyhole edge were determined respectively.展开更多
To overcome some drawbacks of Viterbi algorithm （VA）, such as exponential growing complexity of decoding, and its poor performance under bad channel conditions, some available known information must be used as cons...To overcome some drawbacks of Viterbi algorithm （VA）, such as exponential growing complexity of decoding, and its poor performance under bad channel conditions, some available known information must be used as constrained condition and apriori knowledge for decoding. A new constrained VA is proposed by adding con- straint bits directly for conventional codec. Compared with the conventional VA, under the bad channel condi- tion, the proposed scheme can improve the peak signal to noise ratio （PSNR） of the decoding image 2--10 dB by changing the number of constrained bits. Experimental results show that it is an efficient error-controlling way for the transmission of set partitioning in hierarchical trees （SPIHT） coded image.展开更多
We start from a realistic half space then use to develop a mathematical asymptotic model for thermal imaging, which we analysis well suited for the design of reconstruction algorithms. We seek to reconstruct thermal a...We start from a realistic half space then use to develop a mathematical asymptotic model for thermal imaging, which we analysis well suited for the design of reconstruction algorithms. We seek to reconstruct thermal anomalies only through their rough features. With this way our proposed algorithms are stable against measurement noise and geometry perturbations. Based on rigorous asymptotic estimates, we first obtain an approximation for the temperature profile which we then use to design noniterative detection algorithms. We show on numerical simulations evidence that they are accurate and robust. Moreover, we provide a mathematical model for ultrasonic temperature imaging, which is an important technique in cancerous tissue ablation therapy.展开更多
It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively dif...It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively difficult to detect vehicles of various scales in traffic scene images,because the vehicles partially obscured by green belts,roadblocks or other vehicles,as well as influence of some low illumination weather.In this paper,we present a model based on Faster ReCNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes.First,we proposed a Retinex-based image adaptive correction algorithm(RIAC)to enhance the traffic images in the dataset to reduce the influence of shadow and illumination,and improve the image quality.Second,in order to improve the feature expression of the backbone network,we conducted Neural Architecture Search(NAS)on the backbone network used for feature extraction of Faster ReCNN to generate the optimal cross-layer connection to extract multi-layer features more effectively.Third,we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets,and improve the robustness of the model for challenging targets such as small scale and severe occlusion.In the implementation of the model,K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model.Our model has been trained and tested on the UN-DETRAC dataset,and the obtained results indicate that our method has art-of-state detection performance.展开更多
Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process...Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.展开更多
To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based...To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication.展开更多
文摘Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.
基金Supported by the National Natural Science Foundation of China(Grant No.51905070).
文摘Full matrix focusing method of ultrasonic phased array has been proved with advantages of good signal-to-noise ratio and imaging resolution in the field of Ultrasonic NDT.However,it is still suffering from the time-consuming data acquisition and processing.In order to solve the problem,two simplified matrix focusing methods are provided in the paper.One provided method is a triangular matrix focusing algorithm based on the principle of reciprocity for the multi-channel ultrasonic system.The other provided method is a trapezoidal matrix focusing algorithm based on the energy weight of the different channel to the focusing area.Time of data acquisition and computational is decreased with the provided simplified matrix focusing methods.In order to prove the validity of two provided algorithms,both side-drilled holes and oblique cracks are used for imaging experiments.The experimental results show that the imaging quality of the triangular matrix focusing algorithm is basically consistent to that of the full matrix focusing method.And imaging quality of the trapezoidal matrix focusing algorithm is slightly reduced with the amount of multi-channel data decreasing.Both data acquisition and computational efficiency using the triangular matrix focusing algorithm and the trapezoidal matrix focusing algorithm have been improved significantly compared with original full matrix focusing method.
文摘Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.
基金Projects(91220301,61175064,61273314)supported by the National Natural Science Foundation of ChinaProject(126648)supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2012170301)supported by the New Teacher Fund for School of Information Science and Engineering,Central South University,China
文摘Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.
文摘A millimeter-wave linear frequency modulated continuous wave(LFM CW)radar is applied to water surface detection.This paper presents the experiment and imaging algorithm.In imaging processing,water surface texture can hardly be seen in the results obtained by traditional imaging algorithm.To solve this problem,we propose a millimeter-wave LFMCW radar imaging algorithm for water surface texture.Different from the traditional imaging algorithm,the proposed imaging algorithm includes two improvements as follows:Firstly,the interference from static targets is removed through a frequency domainfilter;Secondly,the multiplicative noises are reduced by the maximum likelihood estimation method,which is used to estimatethe azimuth spectrum parameters to calculate the energy of water surface echo.Final results show that the proposed algorithmcan obtain water surface texture,which means that the proposed algorithm is superior to the traditional imaging algorithm.
文摘Hepatocellular carcinoma(HCC)is a leading cause of morbidity and mortality worldwide,with rising clinical and economic burden as incidence increases.There are a multitude of evolving treatment options,including locoregional therapies which can be used alone,in combination with each other,or in combination with systemic therapy.These treatment options have shown to be effective in achieving remission,controlling tumor progression,improving disease free and overall survival in patients who cannot undergo resection and providing a bridge to transplant by debulking tumor burden to downstage patients.Following locoregional therapy(LRT),it is crucial to provide treatment response assessment to guide management and liver transplant candidacy.Therefore,Liver Imaging Reporting and Data Systems(LI-RADS)Treatment Response Algorithm(TRA)was created to provide a standardized assessment of HCC following LRT.LIRADS TRA provides a step by step approach to evaluate each lesion independently for accurate tumor assessment.In this review,we provide an overview of different locoregional therapies for HCC,describe the expected post treatment imaging appearance following treatment,and review the LI-RADS TRA with guidance for its application in clinical practice.Unique to other publications,we will also review emerging literature supporting the use of LI-RADS for assessment of HCC treatment response after LRT.
基金supported by the National Natural Science Foundation of China(82020108006 and 81730025 to Chen Zhao,U2001209 to Bo Yan)the Excellent Academic Leaders of Shanghai(18XD1401000 to Chen Zhao)the Natural Science Foundation of Shanghai,China(21ZR1406600 to Weimin Tan).
文摘In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise diagnosis.To address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into consideration.Using the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion enhancement.Pathological consistency loss is also applied to maintain fundus feature integrity,significantly improving image quality.Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after enhancement.In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease classification.The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.
文摘This paper focuses on the general case （GC） airborne bistatic synthetic aperture radar （SAR） data processing, and a new analytical imaging algorithm based on the extended Loffeld＇s bistatic formula （ELBF） is proposed. According to the bistatic SAR geometry, the track decoupling formulas that convert the bistatic geometry to the receiver-referenced geometry in a concise way are derived firstly. Then phase terms of ELBF are decomposed into two independent phase terms as the range phase term and the azimuth phase term in a new way. To get the focusing result, the bistatic deformation （BD） term is compensated in the two-dimensional （2- D） frequency domain, and the space-variances of the range phase term and the azimuth phase term are eliminated by chirp scaling （CS） and chirp z-transform （CZT）, respectively. The effectiveness of the proposed algorithm is verified by the simulation results.
基金National Natural Science Foundation of China (40574020 and 10371012).
文摘In the moment-ratio imaging algorithm, which is based on the theory of healing of a wound, the energy of each strong earthquake is distributed around the epicenter according to certain rules, and the features of the Moment-ratio value R are analyzed as the space and time change, so that the relationships between the moment-ration value R and strong earthquakes can be found. In the present paper, regions divided, hypocenter depths and events completed magnitude analyses were carried out in the Chinese catalogue. By applying the moment-ratio imaging algorithm in which the parameters are adjusted, the processes of anomaly evolution which correspond to the epicenter and the surrounding value R before earthquakes of M≥7.0 since 1966 in different areas of China were analyzed. It was found that the range area and imminent time of a coming earthquake could be confirmed quantita- tively by analyzing the abnormal temporal and spatial variation of the value R The results showed that the temporal and spatial variation of the value R could quantitatively reflect the temporal and spatial factors of a coming strong earthquake as well as the rule of medium rupture.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
基金Türkiye Bilimsel ve Teknolojik Arastırma Kurumu。
文摘This paper introduces a novel lightweight colour image encryption algorithm,specifically designed for resource-constrained environments such as Internet of Things(IoT)devices.As IoT systems become increasingly prevalent,secure and efficient data transmission becomes crucial.The proposed algorithm addresses this need by offering a robust yet resource-efficient solution for image encryption.Traditional image encryption relies on confusion and diffusion steps.These stages are generally implemented linearly,but this work introduces a new RSP(Random Strip Peeling)algorithm for the confusion step,which disrupts linearity in the lightweight category by using two different sequences generated by the 1D Tent Map with varying initial conditions.The diffusion stage then employs an XOR matrix generated by the Logistic Map.Different evaluation metrics,such as entropy analysis,key sensitivity,statistical and differential attacks resistance,and robustness analysis demonstrate the proposed algorithm's lightweight,robust,and efficient.The proposed encryption scheme achieved average metric values of 99.6056 for NPCR,33.4397 for UACI,and 7.9914 for information entropy in the SIPI image dataset.It also exhibits a time complexity of O(2×M×N)for an image of size M×N.
基金Project supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MF049)Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001)the Key R&D Program of Shandong Province, China (Grant No. 2023CXGC010901)。
文摘In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform(QAr T) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QAr T to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of “Monkey”, “Flower”, and “House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate(NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity(UACI) is 33.41%,and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.
文摘Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects.Event cameras,as high temporal resolution bionic cameras,record intensity changes in an asynchronous manner,and their recorded high temporal resolution information can effectively solve the problem of time information loss in motion blur.Existing event-based deblurring methods still face challenges when facing high-speed moving objects.We conducted an in-depth study of the imaging principle of event cameras.We found that the event stream contains excessive noise.The valid information is sparse.Invalid event features hinder the expression of valid features due to the uncertainty of the global threshold.To address this problem,a denoising-based long and short-term memory module(DTM)is designed in this paper.The DTM suppressed the original event information by noise reduction process.Invalid features in the event stream and solves the problem of sparse valid information in the event stream,and it also combines with the long short-term memory module(LSTM),which further enhances the event feature information in the time scale.In addition,through the in-depth understanding of the unique characteristics of event features,it is found that the high-frequency information recorded by event features does not effectively guide the fusion feature deblurring process in the spatial-domain-based feature processing,and for this reason,we introduce the residual fast fourier transform module(RES-FFT)to further enhance the high-frequency characteristics of the fusion features by performing the feature extraction of the fusion features from the perspective of the frequency domain.Ultimately,our proposed event image fusion network based on event denoising and frequency domain feature enhancement(DNEFNET)achieved Peak Signal-to-Noise Ratio(PSNR)/Structural Similarity Index Measure(SSIM)scores of 35.55/0.972 on the GoPro dataset and 38.27/0.975 on the REBlur dataset,achieving the state of the art(SOTA)effect.
基金Project (10776020) supported by the Joint Foundation of the National Natural Science Foundation of China and China Academy of Engineering Physics
文摘In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the quality information. Abundant weld quality information is contained in weld pool and keyhole. Aiming at Nd：YAG laser welding of stainless steel, a coaxial visual sensing system was constructed. The images of weld pool and keyhole were obtained. Based on the gray character of weld pool and keyhole in images, an image processing algorithm was designed. The search start point and search criteria of weld pool and keyhole edge were determined respectively.
文摘To overcome some drawbacks of Viterbi algorithm （VA）, such as exponential growing complexity of decoding, and its poor performance under bad channel conditions, some available known information must be used as constrained condition and apriori knowledge for decoding. A new constrained VA is proposed by adding con- straint bits directly for conventional codec. Compared with the conventional VA, under the bad channel condi- tion, the proposed scheme can improve the peak signal to noise ratio （PSNR） of the decoding image 2--10 dB by changing the number of constrained bits. Experimental results show that it is an efficient error-controlling way for the transmission of set partitioning in hierarchical trees （SPIHT） coded image.
基金supported by the ANR project EchoScan(AN-06-Blan-0089)the NSF grant DMS 0707421.
文摘We start from a realistic half space then use to develop a mathematical asymptotic model for thermal imaging, which we analysis well suited for the design of reconstruction algorithms. We seek to reconstruct thermal anomalies only through their rough features. With this way our proposed algorithms are stable against measurement noise and geometry perturbations. Based on rigorous asymptotic estimates, we first obtain an approximation for the temperature profile which we then use to design noniterative detection algorithms. We show on numerical simulations evidence that they are accurate and robust. Moreover, we provide a mathematical model for ultrasonic temperature imaging, which is an important technique in cancerous tissue ablation therapy.
基金funded by the National Natural Science Foundation of China(grant number:61671470)the Key Research and Development Program of China(grant number:2016YFC0802900).
文摘It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively difficult to detect vehicles of various scales in traffic scene images,because the vehicles partially obscured by green belts,roadblocks or other vehicles,as well as influence of some low illumination weather.In this paper,we present a model based on Faster ReCNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes.First,we proposed a Retinex-based image adaptive correction algorithm(RIAC)to enhance the traffic images in the dataset to reduce the influence of shadow and illumination,and improve the image quality.Second,in order to improve the feature expression of the backbone network,we conducted Neural Architecture Search(NAS)on the backbone network used for feature extraction of Faster ReCNN to generate the optimal cross-layer connection to extract multi-layer features more effectively.Third,we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets,and improve the robustness of the model for challenging targets such as small scale and severe occlusion.In the implementation of the model,K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model.Our model has been trained and tested on the UN-DETRAC dataset,and the obtained results indicate that our method has art-of-state detection performance.
基金Project(2017YFC1405600)supported by the National Key R&D Program of ChinaProject(18JK05032)supported by the Scientific Research Project of Education Department of Shaanxi Province,China。
文摘Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.
基金the Basic Scientific Research Projects of Colleges and Universities of Liaoning Province (Grant Nos. 2017J045)Provincial Natural Science Foundation of Liaoning (Grant Nos. 20170540060)
文摘To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication.
