Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to...Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.展开更多
A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, s...A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.展开更多
To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolu...To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.展开更多
Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence ...Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.展开更多
This paper introduces a new effective method to restore the uniform linear motion blurred im-age. The effect of the out-of-frame pixels on the blurring process and the estimate of these pixelsare analysed. The restora...This paper introduces a new effective method to restore the uniform linear motion blurred im-age. The effect of the out-of-frame pixels on the blurring process and the estimate of these pixelsare analysed. The restoration qualities of different deblurring methods are compared. Finally, theauthors come to a conclusion that it is impossible to determine the length of blurring movement infrequency domain.展开更多
An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Point...An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.展开更多
Cone photoreceptor cell identication is important for the early diagnosis of retinopathy.In this study,an object detection algorithm is used for cone cell identication in confocal adaptive optics scanning laser ophtha...Cone photoreceptor cell identication is important for the early diagnosis of retinopathy.In this study,an object detection algorithm is used for cone cell identication in confocal adaptive optics scanning laser ophthalmoscope(AOSLO)images.An effectiveness evaluation of identication using the proposed method reveals precision,recall,and F_(1)-score of 95.8%,96.5%,and 96.1%,respectively,considering manual identication as the ground truth.Various object detection and identication results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method.Overall,the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images,being comparable to manual identication.展开更多
A new method of artificial intelligence based on a new improved back propagation neural network (BPNN) algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues ...A new method of artificial intelligence based on a new improved back propagation neural network (BPNN) algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc. , different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The meth- od applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in im- proving the efficiency overall both in visual effect and quality analysis.展开更多
Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, d...Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.展开更多
A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region accor...A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.展开更多
A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in trans...A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in transform-domain. Then, an iterative algorithm is presented to solve the deconvolution. Because the window-function is available to describe arbitrary shape, our algorithm is suitable for restoring irregular segment of data loss, including square-block. Finally, several simulation tests are done and results prove that the algorithm is valid.展开更多
In this paper,a new direct optical triangulation(DOT) for measuring theout-of-plane displacement is given.In order to state its principle,DOT is used to measure a micro-displacement of a rigid body,and at the same tim...In this paper,a new direct optical triangulation(DOT) for measuring theout-of-plane displacement is given.In order to state its principle,DOT is used to measure a micro-displacement of a rigid body,and at the same time,the method of digital image processing is also given.展开更多
Genetic algorithm is a search algorithm based on genetic mechanism and natural selection. It has been widely applied to research fields including image processing field. The paper improves standard genetic algorithm a...Genetic algorithm is a search algorithm based on genetic mechanism and natural selection. It has been widely applied to research fields including image processing field. The paper improves standard genetic algorithm and improves the arithmetic speed of the algorithm, which achieves better image restoration effect. And the paper compares the image restoration quality of traditional algorithm, standard genetic algorithm and improved genetic algorithm to prove the feasibility of applying genetic algorithm to image restoration.展开更多
Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications.In general,image compression can introduce undesired coding artifacts,...Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications.In general,image compression can introduce undesired coding artifacts,such as blocking artifacts and ringing effects.In this paper,we proposed a Multi-Scale Feature Attention Network(MSFAN)with two essential parts,which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images.Multiscale feature extraction layers have four Feature Extraction(FE)blocks.Each FE block consists of five convolution layers and one CA block for weighted skip connection.In order to optimize the proposed network architectures,a variety of verification tests were conducted using validation dataset.We used Computer Vision Center-Clinic Database(CVC-ClinicDB)consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration.The proposedMSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNNand DCSC,respectively.展开更多
Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages ...Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages of the two-dimensional variational mode decomposition(2DVMD)algorithm and dark channel prior.The original hazy image is adaptively decom-posed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm.The low-frequency image is dehazed by using the improved dark channel prior,and then fused with the high-frequency image.Furthermore,we optimize the atmospheric light and transmit-tance estimation method to obtain a defogging effect with richer details and stronger contrast.The proposed algorithm is com-pared with the existing advanced algorithms.Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.展开更多
Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas.Although its performance has been im...Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas.Although its performance has been improved significantly using diverse convolutional neural network(CNN)-based models,these models have difficulty filling in some erased areas due to the kernel size of the CNN.If the kernel size is too narrow for the blank area,the models cannot consider the entire surrounding area,only partial areas or none at all.This issue leads to typical problems of inpainting,such as pixel reconstruction failure and unintended filling.To alleviate this,in this paper,we propose a novel inpainting model called UFC-net that reinforces two components in U-net.The first component is the latent networks in the middle of U-net to consider the entire surrounding area.The second component is the Hadamard identity skip connection to improve the attention of the inpainting model on the blank areas and reduce computational cost.We performed extensive comparisons with other inpainting models using the Places2 dataset to evaluate the effectiveness of the proposed scheme.We report some of the results.展开更多
Assessment of human airway humen opening is important in diagnosing and understanding the mechanisms of airway dysfunctions such as the excessive airway narrowing in asthma and chronic obstructive pulmonary disease(CO...Assessment of human airway humen opening is important in diagnosing and understanding the mechanisms of airway dysfunctions such as the excessive airway narrowing in asthma and chronic obstructive pulmonary disease(COPD).Although there are indirect methods to evaluate the airway calibre,direct in vivo measurement of the airway calibre has not been commonly available.With recent advent of the flexible fiber optical nasopharyngoscope with video recording it has become possible to directly visualize the passages of upper and lower airways.However,quan-titative analysis of the recorded video images has been technically challenging.Here,we describe an automatic image processing and analysis method that allows for batch analysis of the images recorded during the endoscopic procedure,thus facilitates image-based quantification of the airway opening.Video images of the airway lumen of volunteer subject were acquired using a fiber optical nasopharyngoscope,and subsequently processed using Gaussian smoothing filter,threshold segment ation,differentiation,and Canny image edge detection,respectively.Thus the area of the open airway lumen was identified and computed using.a predetermined converter of the image scale to true dimension of the imaged object.With this method we measured the opening/narrowing of the glottis during tidal breathing with or without making“Hee"sound or cough.We also used this met hod to measure the opening/narrowing of the primary bronchus of either healthy or asthmatic subjects in response to hist amine and/or albuterol treatment,which also provided an indicator of the airway contractility.Our results demonstrate that the image-based method accurately quantifed the area change waveform of either the glottis or the bronchus as observed by using the optical nasopharygoscope.Importantly,the opening/nar-rowing of the airway lumen generally correlated with the airAow and resistance of the airways,and could differentiate the level of airway contr actility between the healthy and asthmatic subjects.Thus,this quant itative assessment of airway opening may provide a useful tool to ssist clinical diagnosis of airway dysfunctions and understanding the mechanisms of associated pathophysiologies.展开更多
Three-dimensional(3D)image reconstruction involves the computations of an extensive amount of data that leads to tremendous processing time.Therefore,optimization is crucially needed to improve the performance and eff...Three-dimensional(3D)image reconstruction involves the computations of an extensive amount of data that leads to tremendous processing time.Therefore,optimization is crucially needed to improve the performance and efficiency.With the widespread use of graphics processing units(GPU),parallel computing is transforming this arduous reconstruction process for numerous imaging modalities,and photoacoustic computed tomography(PACT)is not an exception.Existing works have investigated GPU-based optimization on photoacoustic microscopy(PAM)and PACT reconstruction using compute unified device architecture(CUDA)on either C++or MATLAB only.However,our study is the first that uses cross-platform GPU computation.It maintains the simplicity of MATLAB,while improves the speed through CUDA/C++−based MATLAB converted functions called MEXCUDA.Compared to a purely MATLAB with GPU approach,our cross-platform method improves the speed five times.Because MATLAB is widely used in PAM and PACT,this study will open up new avenues for photoacoustic image reconstruction and relevant real-time imaging applications.展开更多
文摘Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.
文摘A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.
文摘To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.
基金Supported by National Key Research and Development Program of China(2016YFF0201005)。
文摘Based on anisotropic total variation regularization(ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography(OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image.Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.
文摘This paper introduces a new effective method to restore the uniform linear motion blurred im-age. The effect of the out-of-frame pixels on the blurring process and the estimate of these pixelsare analysed. The restoration qualities of different deblurring methods are compared. Finally, theauthors come to a conclusion that it is impossible to determine the length of blurring movement infrequency domain.
文摘An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.
基金the Natural Science Foundation of Jiangsu Province(BK20200214)National Key R&D Program of China(2017YFB0403701)+5 种基金Jiangsu Province Key R&D Program(BE2019682 and BE2018667)National Natural Science Foundation of China(61605210,61675226,and 62075235)Youth Innovation Promotion Association of Chinese Academy of Sciences(2019320)Frontier Science Research Project of the Chinese Academy of Sciences(QYZDB-SSW-JSC03)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060000)and Entrepreneurship and Innova-tion Talents in Jiangsu Province(Innovation of Scienti¯c Research Institutes).
文摘Cone photoreceptor cell identication is important for the early diagnosis of retinopathy.In this study,an object detection algorithm is used for cone cell identication in confocal adaptive optics scanning laser ophthalmoscope(AOSLO)images.An effectiveness evaluation of identication using the proposed method reveals precision,recall,and F_(1)-score of 95.8%,96.5%,and 96.1%,respectively,considering manual identication as the ground truth.Various object detection and identication results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method.Overall,the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images,being comparable to manual identication.
基金Supported by the National Natural Science Foundation of China (60772066)Higher Education Commission of Pakistan
文摘A new method of artificial intelligence based on a new improved back propagation neural network (BPNN) algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc. , different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The meth- od applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in im- proving the efficiency overall both in visual effect and quality analysis.
基金financial support from the Brazilian Federal Agency for Support and Evaluation of Graduate Education(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior—CAPES,scholarship process no BEX 0506/15-0)the Brazilian National Agency of Petroleum,Natural Gas and Biofuels(Agencia Nacional do Petroleo,Gas Natural e Biocombustiveis—ANP),in cooperation with the Brazilian Financier of Studies and Projects(Financiadora de Estudos e Projetos—FINEP)the Brazilian Ministry of Science,Technology and Innovation(Ministério da Ciencia,Tecnologia e Inovacao—MCTI)through the ANP’s Human Resources Program of the State University of Sao Paulo(Universidade Estadual Paulista—UNESP)for the Oil and Gas Sector PRH-ANP/MCTI no 48(PRH48).
文摘Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.
文摘A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.
基金Supported by the National Natural Science Foundation of China (No.60072012).
文摘A novel scheme for image data restoration is proposed in this letter. First, a window- function model is exploited to describe the data loss in images. It can change the restoration problem into deconvolution in transform-domain. Then, an iterative algorithm is presented to solve the deconvolution. Because the window-function is available to describe arbitrary shape, our algorithm is suitable for restoring irregular segment of data loss, including square-block. Finally, several simulation tests are done and results prove that the algorithm is valid.
文摘In this paper,a new direct optical triangulation(DOT) for measuring theout-of-plane displacement is given.In order to state its principle,DOT is used to measure a micro-displacement of a rigid body,and at the same time,the method of digital image processing is also given.
文摘Genetic algorithm is a search algorithm based on genetic mechanism and natural selection. It has been widely applied to research fields including image processing field. The paper improves standard genetic algorithm and improves the arithmetic speed of the algorithm, which achieves better image restoration effect. And the paper compares the image restoration quality of traditional algorithm, standard genetic algorithm and improved genetic algorithm to prove the feasibility of applying genetic algorithm to image restoration.
基金This work was supported by Kyungnam University Foundation Grant,2020.
文摘Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications.In general,image compression can introduce undesired coding artifacts,such as blocking artifacts and ringing effects.In this paper,we proposed a Multi-Scale Feature Attention Network(MSFAN)with two essential parts,which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images.Multiscale feature extraction layers have four Feature Extraction(FE)blocks.Each FE block consists of five convolution layers and one CA block for weighted skip connection.In order to optimize the proposed network architectures,a variety of verification tests were conducted using validation dataset.We used Computer Vision Center-Clinic Database(CVC-ClinicDB)consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration.The proposedMSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNNand DCSC,respectively.
基金supported by the National Defense Technology Advance Research Project of China(004040204).
文摘Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages of the two-dimensional variational mode decomposition(2DVMD)algorithm and dark channel prior.The original hazy image is adaptively decom-posed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm.The low-frequency image is dehazed by using the improved dark channel prior,and then fused with the high-frequency image.Furthermore,we optimize the atmospheric light and transmit-tance estimation method to obtain a defogging effect with richer details and stronger contrast.The proposed algorithm is com-pared with the existing advanced algorithms.Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.
基金This research was supported in part by NRF(National Research Foundation of Korea)Grant funded by the Korean Government(No.NRF-2020R1F1A1074885)and in part by the Brain Korea 21 FOUR Project in 2021.
文摘Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas.Although its performance has been improved significantly using diverse convolutional neural network(CNN)-based models,these models have difficulty filling in some erased areas due to the kernel size of the CNN.If the kernel size is too narrow for the blank area,the models cannot consider the entire surrounding area,only partial areas or none at all.This issue leads to typical problems of inpainting,such as pixel reconstruction failure and unintended filling.To alleviate this,in this paper,we propose a novel inpainting model called UFC-net that reinforces two components in U-net.The first component is the latent networks in the middle of U-net to consider the entire surrounding area.The second component is the Hadamard identity skip connection to improve the attention of the inpainting model on the blank areas and reduce computational cost.We performed extensive comparisons with other inpainting models using the Places2 dataset to evaluate the effectiveness of the proposed scheme.We report some of the results.
基金supported by grants from Natural Science Foundation of China(Grant No.11172340)Training Program for Hundreds of Distinguished Leading Scientists of Chongqing,Chongqing Natural Science Foundation(Project No.CSTC,2010BA5001)Sharing Fund of Chongqing University Large-Scale Equipment(Nos.2010063057,2011063048,and 2011063049).
文摘Assessment of human airway humen opening is important in diagnosing and understanding the mechanisms of airway dysfunctions such as the excessive airway narrowing in asthma and chronic obstructive pulmonary disease(COPD).Although there are indirect methods to evaluate the airway calibre,direct in vivo measurement of the airway calibre has not been commonly available.With recent advent of the flexible fiber optical nasopharyngoscope with video recording it has become possible to directly visualize the passages of upper and lower airways.However,quan-titative analysis of the recorded video images has been technically challenging.Here,we describe an automatic image processing and analysis method that allows for batch analysis of the images recorded during the endoscopic procedure,thus facilitates image-based quantification of the airway opening.Video images of the airway lumen of volunteer subject were acquired using a fiber optical nasopharyngoscope,and subsequently processed using Gaussian smoothing filter,threshold segment ation,differentiation,and Canny image edge detection,respectively.Thus the area of the open airway lumen was identified and computed using.a predetermined converter of the image scale to true dimension of the imaged object.With this method we measured the opening/narrowing of the glottis during tidal breathing with or without making“Hee"sound or cough.We also used this met hod to measure the opening/narrowing of the primary bronchus of either healthy or asthmatic subjects in response to hist amine and/or albuterol treatment,which also provided an indicator of the airway contractility.Our results demonstrate that the image-based method accurately quantifed the area change waveform of either the glottis or the bronchus as observed by using the optical nasopharygoscope.Importantly,the opening/nar-rowing of the airway lumen generally correlated with the airAow and resistance of the airways,and could differentiate the level of airway contr actility between the healthy and asthmatic subjects.Thus,this quant itative assessment of airway opening may provide a useful tool to ssist clinical diagnosis of airway dysfunctions and understanding the mechanisms of associated pathophysiologies.
基金supported in part by the Career Catalyst Research Grant from the Susan G.Komen Foundationthe Clinical and Translational Science Pilot Study Award from the National Institutes of Health.
文摘Three-dimensional(3D)image reconstruction involves the computations of an extensive amount of data that leads to tremendous processing time.Therefore,optimization is crucially needed to improve the performance and efficiency.With the widespread use of graphics processing units(GPU),parallel computing is transforming this arduous reconstruction process for numerous imaging modalities,and photoacoustic computed tomography(PACT)is not an exception.Existing works have investigated GPU-based optimization on photoacoustic microscopy(PAM)and PACT reconstruction using compute unified device architecture(CUDA)on either C++or MATLAB only.However,our study is the first that uses cross-platform GPU computation.It maintains the simplicity of MATLAB,while improves the speed through CUDA/C++−based MATLAB converted functions called MEXCUDA.Compared to a purely MATLAB with GPU approach,our cross-platform method improves the speed five times.Because MATLAB is widely used in PAM and PACT,this study will open up new avenues for photoacoustic image reconstruction and relevant real-time imaging applications.