目的有效滤除带钢表面缺陷图像高斯噪声。方法高斯噪声是影响带钢图像质量的主要噪声类型之一,针对带钢表面缺陷图像高斯噪声去噪,首先对传统K-SVD(K-means and singular value decomposition)算法中的字典进行升级改造,然后采用正交匹...目的有效滤除带钢表面缺陷图像高斯噪声。方法高斯噪声是影响带钢图像质量的主要噪声类型之一,针对带钢表面缺陷图像高斯噪声去噪,首先对传统K-SVD(K-means and singular value decomposition)算法中的字典进行升级改造,然后采用正交匹配追踪(OMP,Orthogonal Matching Pursuit)算法对图像进行重构,滤除噪声,最后运用此算法对缺陷图像进行高斯滤波处理。为验证该算法去噪效果,选取几种常见的典型缺陷图像(划伤、气泡、氧化色、粘结纹)进行测试仿真,并选用中值滤波、均值滤波、小波变换、维纳滤波、3维块匹配(BM3D)等多种传统滤波方法进行比较。结果该算法对四种典型缺陷去噪的PSNR(Peak Signal to Noise Ratio)值平均可达33.976 d B,MSE(Mean Square Error)平均值为27.607,SSIM(Structural Similarity)平均值为0.912。结论该算法对带钢表面缺陷重构图像的边缘细节清晰,PSNR、MSE、SSIM三个性能指标明显优于其他传统滤波算法,去噪效果良好。展开更多
Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based a...Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high frequency information of the test image to achieve the purpose of image super-resolution reconstruction. This paper presents a novel algorithm for image super resolution based on morphological component analysis (MCA) and dictionary learning. The MCA decomposition based SR algorithm utilizes MCA to decompose an image into the texture part and the structure part and only takes the texture part to train the dictionary. The reconstruction of the texture part is based on sparse representation, while that of the structure part is based on more faster method, the bicubic interpolation. The proposed method improves the robustness of the image, while for different characteristics of textures and structure parts, using a different reconstruction algorithm, better preserves image details, improve the quality of the reconstructed image.展开更多
This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information(G-LS-SVM)to solve the insulated-gate bipolar transistor(IGBT)open-circuit failure problem...This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information(G-LS-SVM)to solve the insulated-gate bipolar transistor(IGBT)open-circuit failure problem of the traction inverter in a catenary power supply system.First,a simulation model based on traction inverter topology is built,and various voltage fault signal waveforms are simulated based on the IGBT inverter open-circuit fault classification.Second,compressive sensing theory is used to sparsely represent the voltage fault signal and make it a fault signal.The new method has a high degree of sparseness and builds an overcomplete dictionary model containing the feature vectors of voltage fault signals based on a double sparse dictionary model to match the sparse signal characteristics.Finally,the space vector transform is used to represent the three-phase voltage scalar in the traction inverter as a composite quantity to reduce the redundancy of the fault signals and data-processing capabilities.A G-LS-SVM fault diagnosis model is then built to diagnose and identify the voltage fault signal feature vector in an overcomplete dictionary.The simulation results show that the accuracy of this method for various types of IGBT tube fault diagnosis is over 98.92%.Moreover,the G-LS-SVM model is robust and not affected by Gaussian white noise.展开更多
文摘目的有效滤除带钢表面缺陷图像高斯噪声。方法高斯噪声是影响带钢图像质量的主要噪声类型之一,针对带钢表面缺陷图像高斯噪声去噪,首先对传统K-SVD(K-means and singular value decomposition)算法中的字典进行升级改造,然后采用正交匹配追踪(OMP,Orthogonal Matching Pursuit)算法对图像进行重构,滤除噪声,最后运用此算法对缺陷图像进行高斯滤波处理。为验证该算法去噪效果,选取几种常见的典型缺陷图像(划伤、气泡、氧化色、粘结纹)进行测试仿真,并选用中值滤波、均值滤波、小波变换、维纳滤波、3维块匹配(BM3D)等多种传统滤波方法进行比较。结果该算法对四种典型缺陷去噪的PSNR(Peak Signal to Noise Ratio)值平均可达33.976 d B,MSE(Mean Square Error)平均值为27.607,SSIM(Structural Similarity)平均值为0.912。结论该算法对带钢表面缺陷重构图像的边缘细节清晰,PSNR、MSE、SSIM三个性能指标明显优于其他传统滤波算法,去噪效果良好。
文摘Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high frequency information of the test image to achieve the purpose of image super-resolution reconstruction. This paper presents a novel algorithm for image super resolution based on morphological component analysis (MCA) and dictionary learning. The MCA decomposition based SR algorithm utilizes MCA to decompose an image into the texture part and the structure part and only takes the texture part to train the dictionary. The reconstruction of the texture part is based on sparse representation, while that of the structure part is based on more faster method, the bicubic interpolation. The proposed method improves the robustness of the image, while for different characteristics of textures and structure parts, using a different reconstruction algorithm, better preserves image details, improve the quality of the reconstructed image.
基金Supported by National Key R&D Program of China(No.2017YFB1201003-20)。
文摘This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information(G-LS-SVM)to solve the insulated-gate bipolar transistor(IGBT)open-circuit failure problem of the traction inverter in a catenary power supply system.First,a simulation model based on traction inverter topology is built,and various voltage fault signal waveforms are simulated based on the IGBT inverter open-circuit fault classification.Second,compressive sensing theory is used to sparsely represent the voltage fault signal and make it a fault signal.The new method has a high degree of sparseness and builds an overcomplete dictionary model containing the feature vectors of voltage fault signals based on a double sparse dictionary model to match the sparse signal characteristics.Finally,the space vector transform is used to represent the three-phase voltage scalar in the traction inverter as a composite quantity to reduce the redundancy of the fault signals and data-processing capabilities.A G-LS-SVM fault diagnosis model is then built to diagnose and identify the voltage fault signal feature vector in an overcomplete dictionary.The simulation results show that the accuracy of this method for various types of IGBT tube fault diagnosis is over 98.92%.Moreover,the G-LS-SVM model is robust and not affected by Gaussian white noise.
