Image processing is becoming more popular because images are being used increasingly in medical diagnosis,biometric monitoring,and character recognition.But these images are frequently contaminated with noise,which ca...Image processing is becoming more popular because images are being used increasingly in medical diagnosis,biometric monitoring,and character recognition.But these images are frequently contaminated with noise,which can corrupt subsequent image processing stages.Therefore,in this paper,we propose a novel nonlinear filter for removing“salt and pepper”impulsive noise from a complex color image.The new filter is called the Modified Vector Directional Filter(MVDF).The suggested method is based on the traditional Vector Directional Filter(VDF).However,before the candidate pixel is processed by the VDF,theMVDF employs a threshold and the neighboring pixels of the candidate pixel in a 3×3 filter window to determine whether it is noise-corrupted or noise-free.Several reference color images corrupted by impulsive noise with intensities ranging from 3%to 20%are used to assess theMVDF’s effectiveness.The results of the experiments show that theMVDF is better than the VDF and the Generalized VDF(GVDF)in terms of the PSNR(Peak Signal-to-Noise Ratio),NCD(Normalized Color Difference),and execution time for the denoised image.In fact,the PSNR is increased by 6.554%and 12.624%,the NCD is decreased by 20.273%and 44.147%,and the execution time is reduced by approximately a factor of 3 for the MVDF relative to the VDF and GVDF,respectively.These results prove the efficiency of the proposed filter.Furthermore,a hardware design is proposed for the MVDF using the High-Level Synthesis(HLS)flow in order to increase its performance.This design,which is implemented on the Xilinx ZynqXCZU9EG Field-ProgrammableGate Array(FPGA),allows the restoration of a 256×256-pixel image in 2 milliseconds(ms)only.展开更多
Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be ma...Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.展开更多
Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at an...Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.展开更多
Through the analysis on the earth landscape colors in the beautiful countryside,this paper stated that the color is the most sensitive element when people touch the countryside. The quality of landscape color directly...Through the analysis on the earth landscape colors in the beautiful countryside,this paper stated that the color is the most sensitive element when people touch the countryside. The quality of landscape color directly influences the people's mood when appreciating the beautiful countryside. Taking respecting natural environment,extending the historical context,and conforming to aesthetic taste as the precondition,this paper made optimal design of earth landscape colors in beautiful countryside using the hue unifying method,color contrast method,and color rhyme balancing method.展开更多
IHS （Intensity, Hue and Saturation） transform is one of the most commonly used tusion algonthm. But the matching error causes spectral distortion and degradation in processing of image fusion with IHS method. A stud...IHS （Intensity, Hue and Saturation） transform is one of the most commonly used tusion algonthm. But the matching error causes spectral distortion and degradation in processing of image fusion with IHS method. A study on IHS fusion indicates that the color distortion can＇t be avoided. Meanwhile, the statistical property of wavelet coefficient with wavelet decomposition reflects those significant features, such as edges, lines and regions. So, a united optimal fusion method, which uses the statistical property and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component Ⅰ is fused on feature level with multi-resolution wavelet in IHS space. And the low frequency of intensity component Ⅰ is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results with QuickBird data of Shanghai show that it is a practical and effective method.展开更多
How to choose modulation frequency in the extra-high frequency geodimeter is directly related to a lot key matters such as distance sensitivity of instruments and degree of technological difficulty of modulation syste...How to choose modulation frequency in the extra-high frequency geodimeter is directly related to a lot key matters such as distance sensitivity of instruments and degree of technological difficulty of modulation system. This paper analyzes and proves that choice range of modulation frequency can be from 800 MHz up to extra-high frequency with the展开更多
An optimal algorithm for the retrieval of chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary was established with the optical parameters derived from the in-situ data obtained ...An optimal algorithm for the retrieval of chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary was established with the optical parameters derived from the in-situ data obtained in Jan. 2003 in the same area. And then, the chlorophyll, suspended sediments and gelbstoff of the SeaWiFS pixels on Jan. 29, 2003 corresponding to the in-situ sites of Jan. 25 and 26, 2003 were synchronously retrieved, with average relative errors of 14.9%, 12.1% and 13.6% for chlorophyll, suspended sediments and gelbstoff, respectively. The research results indicated that the optimal retrieval algorithm established here was relatively fit for the retrieval of the chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary, and had quite good retrieval accuracy.展开更多
In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonne...In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.展开更多
The visual features of continuous pseudocolor encoding is discussed and the optimiz- ing design algorithm of continuous pseudocolor scale is derived.The algorithm is restricting the varying range and direction of ligh...The visual features of continuous pseudocolor encoding is discussed and the optimiz- ing design algorithm of continuous pseudocolor scale is derived.The algorithm is restricting the varying range and direction of lightness,hue and saturation according to correlation and naturalness,automatically calculating the chromaticity coordinates of nodes in uniform color space to get the longest length of scale path,then interpolating points between nodes in equal color differences to obtain continuous pseudocolor scale with visual uniformity.When it was applied to the pseudocolor encoding of thermal image displays,the results showed that the correlation and the naturalness of original images and cognitive characteristics of target pattern were reserved well;the dynamic range of visual perception and the amount of visual information increased obviously;the contrast sensitivity of target identification improved;and the blindness of scale design were avoided.展开更多
Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity.Saliency detection based on image intrinsic cues can achieve fast detection,but with poo...Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity.Saliency detection based on image intrinsic cues can achieve fast detection,but with poor accuracy.Recent studies reveal that optimization-based methods provide accurate and quick solutions for saliency detection.This paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue region.It can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and robustness.Experimental results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic cues.The algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.展开更多
基金funded by the Deanship of Scientific Research at Jouf University (Kingdom of Saudi Arabia)under Grant No.DSR-2021-02-0393.
文摘Image processing is becoming more popular because images are being used increasingly in medical diagnosis,biometric monitoring,and character recognition.But these images are frequently contaminated with noise,which can corrupt subsequent image processing stages.Therefore,in this paper,we propose a novel nonlinear filter for removing“salt and pepper”impulsive noise from a complex color image.The new filter is called the Modified Vector Directional Filter(MVDF).The suggested method is based on the traditional Vector Directional Filter(VDF).However,before the candidate pixel is processed by the VDF,theMVDF employs a threshold and the neighboring pixels of the candidate pixel in a 3×3 filter window to determine whether it is noise-corrupted or noise-free.Several reference color images corrupted by impulsive noise with intensities ranging from 3%to 20%are used to assess theMVDF’s effectiveness.The results of the experiments show that theMVDF is better than the VDF and the Generalized VDF(GVDF)in terms of the PSNR(Peak Signal-to-Noise Ratio),NCD(Normalized Color Difference),and execution time for the denoised image.In fact,the PSNR is increased by 6.554%and 12.624%,the NCD is decreased by 20.273%and 44.147%,and the execution time is reduced by approximately a factor of 3 for the MVDF relative to the VDF and GVDF,respectively.These results prove the efficiency of the proposed filter.Furthermore,a hardware design is proposed for the MVDF using the High-Level Synthesis(HLS)flow in order to increase its performance.This design,which is implemented on the Xilinx ZynqXCZU9EG Field-ProgrammableGate Array(FPGA),allows the restoration of a 256×256-pixel image in 2 milliseconds(ms)only.
基金Strategic Priority Research Program of Chinese Academy of Sciences,Grant No.XDA28040000,XDA28120000Natural Science Foundation of Shandong Province,Grant No.ZR2021MF094+2 种基金Key R&D Plan of Shandong Province,Grant No.2020CXGC010804Central Leading Local Science and Technology Development Special Fund Project,Grant No.YDZX2021122Science&Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta,Grant No.2022SZX11。
文摘Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR11).
文摘Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.
基金Supported by the Postgraduate Innovation Fund Project of Jiangxi Normal University in 2016(YC2016-S153)Scientific Research Project of Higher Learning Institutions in Jiangxi Province(JXJG-15-2-29)
文摘Through the analysis on the earth landscape colors in the beautiful countryside,this paper stated that the color is the most sensitive element when people touch the countryside. The quality of landscape color directly influences the people's mood when appreciating the beautiful countryside. Taking respecting natural environment,extending the historical context,and conforming to aesthetic taste as the precondition,this paper made optimal design of earth landscape colors in beautiful countryside using the hue unifying method,color contrast method,and color rhyme balancing method.
基金Supported by the High Technology Research and Development Programme of China （2001AA135091） and the National Natural Science Foundation of China （60375008）.
文摘IHS （Intensity, Hue and Saturation） transform is one of the most commonly used tusion algonthm. But the matching error causes spectral distortion and degradation in processing of image fusion with IHS method. A study on IHS fusion indicates that the color distortion can＇t be avoided. Meanwhile, the statistical property of wavelet coefficient with wavelet decomposition reflects those significant features, such as edges, lines and regions. So, a united optimal fusion method, which uses the statistical property and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component Ⅰ is fused on feature level with multi-resolution wavelet in IHS space. And the low frequency of intensity component Ⅰ is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results with QuickBird data of Shanghai show that it is a practical and effective method.
文摘How to choose modulation frequency in the extra-high frequency geodimeter is directly related to a lot key matters such as distance sensitivity of instruments and degree of technological difficulty of modulation system. This paper analyzes and proves that choice range of modulation frequency can be from 800 MHz up to extra-high frequency with the
文摘An optimal algorithm for the retrieval of chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary was established with the optical parameters derived from the in-situ data obtained in Jan. 2003 in the same area. And then, the chlorophyll, suspended sediments and gelbstoff of the SeaWiFS pixels on Jan. 29, 2003 corresponding to the in-situ sites of Jan. 25 and 26, 2003 were synchronously retrieved, with average relative errors of 14.9%, 12.1% and 13.6% for chlorophyll, suspended sediments and gelbstoff, respectively. The research results indicated that the optimal retrieval algorithm established here was relatively fit for the retrieval of the chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary, and had quite good retrieval accuracy.
基金The Pre-Research Foundation of National Ministries andCommissions (No9140A16050109DZ01)the Scientific Research Program of the Education Department of Shanxi Province (No09JK701)
文摘In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.
文摘The visual features of continuous pseudocolor encoding is discussed and the optimiz- ing design algorithm of continuous pseudocolor scale is derived.The algorithm is restricting the varying range and direction of lightness,hue and saturation according to correlation and naturalness,automatically calculating the chromaticity coordinates of nodes in uniform color space to get the longest length of scale path,then interpolating points between nodes in equal color differences to obtain continuous pseudocolor scale with visual uniformity.When it was applied to the pseudocolor encoding of thermal image displays,the results showed that the correlation and the naturalness of original images and cognitive characteristics of target pattern were reserved well;the dynamic range of visual perception and the amount of visual information increased obviously;the contrast sensitivity of target identification improved;and the blindness of scale design were avoided.
基金This work was partially supported by Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”,China(No.2018AAA0102403)the National Natural Science Foundation of China(Nos.U1913602,T2121003,91948204,62103040,and U20B2071)the Open Fund/Postdoctoral Fund of the Laboratory of Cognition and Decision Intelligence for Complex Systems,Institute of Automation,Chinese Academy of Sciences(No.CASIA-KFKT-08).
文摘Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity.Saliency detection based on image intrinsic cues can achieve fast detection,but with poor accuracy.Recent studies reveal that optimization-based methods provide accurate and quick solutions for saliency detection.This paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue region.It can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and robustness.Experimental results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic cues.The algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.
