Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification result...Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.展开更多
In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, whi...In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.展开更多
Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are ...Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are some technical difficulties in lift fin stabilizers,such as lift force detection and lift force sensor installation,so it cannot achieve the good antirolling performance.Therefore,a fin stabilizer system with fin-lift/fin-angle integrated control is brought forward.Data fusion based on wavelet denoising technology is employed in the system,which combines lift with fin angle local information from two sensors with different frequency ranges in order to eliminate redundant and contradictory information,and using complementary information to obtain the relative integrity of the lift force signal.The system model is established in this paper,and the fusion signal and the antirolling performance of this model are simulated respectively.The result shows that the control system can meet the antirolling need in different sea situations.展开更多
A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coef...A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coefficients of the source images are combined into the composite NWF transform coefficients. Inverse NWF transform is performed on the composite NWF transform coefficients in order to obtain the intermediate fused image. Finally, intensity adjustment is applied to the intermediate fused image in order to maintain the dynamic intensity range. Experiment resuits using real data show that the proposed algorithm works well in muitisensor image fusion.展开更多
In order to meet the requirements of medical research,diagnosis and treatment,a new algorithm for image fusion based on the wavelet packet transform in conjunction with both subjective and objective assessments is put...In order to meet the requirements of medical research,diagnosis and treatment,a new algorithm for image fusion based on the wavelet packet transform in conjunction with both subjective and objective assessments is put forward in the paper.Compared to the wavelet transform,the wavelet packet transform is more intricate and effective for the medical image fusion.As indicated by the experimental results,parameters of the feedback system of the new algorithm are significantly superior to those of the wavelet transform,with practicability and accuracy.展开更多
Objective This paper proposed a novel algorithm of discrete wavelet transform(DWT) which is used for multimodal medical image fusion. Methods The source medical images are initially transformed by DWT followed by fusi...Objective This paper proposed a novel algorithm of discrete wavelet transform(DWT) which is used for multimodal medical image fusion. Methods The source medical images are initially transformed by DWT followed by fusing low and high frequency sub-images. Then, the "coefficient absolute value" that can provide clear and detail parts is adapted to fuse high-frequency coefficients, where as the "region energy ratio" which can efficiently preserve most information of source images is employed to fuse low-frequency coefficients. Finally, the fused image is reconstructed by inverse wavelet transform. Results Visually and quantitatively experimental results indicate that the proposed fusion method is superior to traditional wavelet transform and the existing fusion methods. Conclusion The proposed method is a feasible approach for multimodal medical image fusion which can obtain more efficient and accurate fusions results even in the noise environment.展开更多
This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficie...This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficients fusion rule which included choice of regional variance and weighted average wavelet information. The result indicates that this method is better than WMF, LEF and RVF on fusion results, details and target distortion.展开更多
With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet tran...With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.展开更多
According to the time-frequency localization characteristic of the wavelet transform (WT)and the nonlinear reflection of the neural network,this paper presents the neural network data fusion fault diagnosis method bas...According to the time-frequency localization characteristic of the wavelet transform (WT)and the nonlinear reflection of the neural network,this paper presents the neural network data fusion fault diagnosis method based on wavelet transform.The network construction and the signal processing steps are introduced in detail.The correct result was attained by using this method in rotary machinery fault diagnosis.It proves the method efficient in fault diagnosis, which is expected to have a wide application.展开更多
In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the ...In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.展开更多
The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and n...The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.展开更多
Sotos syndrome is characterized by overgrowth features and is caused by alterations in the nuclear receptor binding SET domain protein 1 gene.Attentiondeficit/hyperactivity disorder(ADHD)is considered a neurodevelopme...Sotos syndrome is characterized by overgrowth features and is caused by alterations in the nuclear receptor binding SET domain protein 1 gene.Attentiondeficit/hyperactivity disorder(ADHD)is considered a neurodevelopment and psychiatric disorder in childhood.Genetic characteristics and clinical presentation could play an important role in the diagnosis of Sotos syndrome and ADHD.Magnetic resonance imaging(MRI)has been used to assess medical images in Sotos syndrome and ADHD.The images process is considered to display in MRI while wavelet fusion has been used to integrate distinct images for achieving more complete information in single image in this editorial.In the future,genetic mechanisms and artificial intelligence related to medical images could be used in the clinical diagnosis of Sotos syndrome and ADHD.展开更多
文摘Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.
基金Sponsored by the Nation Nature Science Foundation of China(Grant No.61275010,61201237)the Fundamental Research Funds for the Central Universities(Grant No.HEUCFZ1129,No.HEUCF120805)
文摘In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.
基金supported by the "Ship Control Engineering" Emphasis Project of 211 Engineering in the Tenth Five-Year Plan
文摘Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are some technical difficulties in lift fin stabilizers,such as lift force detection and lift force sensor installation,so it cannot achieve the good antirolling performance.Therefore,a fin stabilizer system with fin-lift/fin-angle integrated control is brought forward.Data fusion based on wavelet denoising technology is employed in the system,which combines lift with fin angle local information from two sensors with different frequency ranges in order to eliminate redundant and contradictory information,and using complementary information to obtain the relative integrity of the lift force signal.The system model is established in this paper,and the fusion signal and the antirolling performance of this model are simulated respectively.The result shows that the control system can meet the antirolling need in different sea situations.
文摘A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coefficients of the source images are combined into the composite NWF transform coefficients. Inverse NWF transform is performed on the composite NWF transform coefficients in order to obtain the intermediate fused image. Finally, intensity adjustment is applied to the intermediate fused image in order to maintain the dynamic intensity range. Experiment resuits using real data show that the proposed algorithm works well in muitisensor image fusion.
文摘In order to meet the requirements of medical research,diagnosis and treatment,a new algorithm for image fusion based on the wavelet packet transform in conjunction with both subjective and objective assessments is put forward in the paper.Compared to the wavelet transform,the wavelet packet transform is more intricate and effective for the medical image fusion.As indicated by the experimental results,parameters of the feedback system of the new algorithm are significantly superior to those of the wavelet transform,with practicability and accuracy.
文摘Objective This paper proposed a novel algorithm of discrete wavelet transform(DWT) which is used for multimodal medical image fusion. Methods The source medical images are initially transformed by DWT followed by fusing low and high frequency sub-images. Then, the "coefficient absolute value" that can provide clear and detail parts is adapted to fuse high-frequency coefficients, where as the "region energy ratio" which can efficiently preserve most information of source images is employed to fuse low-frequency coefficients. Finally, the fused image is reconstructed by inverse wavelet transform. Results Visually and quantitatively experimental results indicate that the proposed fusion method is superior to traditional wavelet transform and the existing fusion methods. Conclusion The proposed method is a feasible approach for multimodal medical image fusion which can obtain more efficient and accurate fusions results even in the noise environment.
文摘This paper describes a method to decompose multi-scale information from different source medical image using wavelet transformation. The data fusion between CT image and MRI image is implemented based on the coefficients fusion rule which included choice of regional variance and weighted average wavelet information. The result indicates that this method is better than WMF, LEF and RVF on fusion results, details and target distortion.
文摘With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.
文摘According to the time-frequency localization characteristic of the wavelet transform (WT)and the nonlinear reflection of the neural network,this paper presents the neural network data fusion fault diagnosis method based on wavelet transform.The network construction and the signal processing steps are introduced in detail.The correct result was attained by using this method in rotary machinery fault diagnosis.It proves the method efficient in fault diagnosis, which is expected to have a wide application.
文摘In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.
文摘The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.
基金Supported by Natural Science Foundation of Shanghai,No.17ZR1431400National Key R and D Program of China,No.2017YFA0103902.
文摘Sotos syndrome is characterized by overgrowth features and is caused by alterations in the nuclear receptor binding SET domain protein 1 gene.Attentiondeficit/hyperactivity disorder(ADHD)is considered a neurodevelopment and psychiatric disorder in childhood.Genetic characteristics and clinical presentation could play an important role in the diagnosis of Sotos syndrome and ADHD.Magnetic resonance imaging(MRI)has been used to assess medical images in Sotos syndrome and ADHD.The images process is considered to display in MRI while wavelet fusion has been used to integrate distinct images for achieving more complete information in single image in this editorial.In the future,genetic mechanisms and artificial intelligence related to medical images could be used in the clinical diagnosis of Sotos syndrome and ADHD.
文摘针对水下环境复杂性带来的多尺度目标检测挑战,提出了改进算法WPS-YOLOv8。设计了小波池化卷积模块(wavelet pooling convolution,WPConv),该模块通过小波池化技术降低通道压缩后特征图的分辨率,有效抑制了传统下采样过程中产生的频率混叠伪影,提升了特征提取质量和表达能力。提出了局部逐点分组重排卷积模块(partial pointwise group shuffle convolution,PGConv),该模块通过结合局部卷积与逐点卷积,能够在减少信息冗余的同时保持通道间的信息交互,弥补了深度可分离卷积的不足,增强了特征融合效果。提出了ShapeLoss损失函数,综合考虑影响不同尺度目标检测精度的因素,通过集成Shape-IoU和Shape-NWD两种损失测度,有效提升了对多尺度目标的总体检测精度。实验结果显示,相较于YOLOv8,WPS-YOLOv8在URPC2018和UTDAC2020水下数据集上的平均精度均值(mean average precision,mAP)分别提升了8.6和4.4个百分点,展现了其在水下多尺度目标检测中的出色表现。
