- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Inter-Subject Image Registration of Clinical Neck MRI Volumes using Discrete Periodic Spline Wavelet and Free form Deformation
摘要： This paper presents a framework for inter-patient image registration which uses a multi-thresholds, multi-similarity measures and multi-transformations based on compactly supported spline and discrete periodic spline wavelets (DPSWs) using the Gauss-Newton gradient descent (GNGD) and gradient descent (GD) optimization methods. Our primary intellectual contribution is incorporating DPSWs in the transformation while another includes fusing out-of-range concept in a surface matching technique which is implemented by a multi-transformations and multi-similarity measures. In particular, as a true deformation cannot be achieved by single combination of transformation, similarity measure (SM) and optimization of a registration process, a moving image is required to be brought within the range of a registration. On the other hand, the surface matching technique involves an edge position difference (EPD) SM in which coarse to fine surfaces are matched using multiple thresholds with a spline-based free from deformation (FFD) method. The registration experiments were performed on 3D clinical neck magnetic resonance (MR) images, with the results showing that our proposed method provides good accuracy and robustness.
关键词： Image registration,edge position difference,multi-resolution,inter-subject registration,optimization,discrete periodic spline wavelet
更新于2025-09-23 15:22:29
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GWDWT-FCM: Change Detection in SAR Images Using Adaptive Discrete Wavelet Transform with Fuzzy C-Mean Clustering
摘要： Change detection in remote sensing images turns out to play a significant role for the preceding years. Change detection in synthetic aperture radar (SAR) images comprises certain complications owing to the reality that it endures from the existence of the speckle noise. Hence, to overcome this limitation, this paper intends to develop an improved model for detecting the changes in SAR image. In this model, two SAR images captivated at varied times will be considered as the input for the change detection process. Initially, discrete wavelet transform (DWT) is employed for image fusion, where the coefficients are optimized using improved grey wolf optimization (GWO) called adaptive GWO (AGWO) algorithm. Finally, the fused images after inverse transform are clustered using fuzzy C-means (FCM) clustering technique and a similarity measure is performed among the segmented image and ground truth image. With the use of all these technologies, the proposed model is termed as adaptive grey wolf-based DWT with FCM (AGWDWT-FCM). The similarity measures analyze the relevant performance measures such as accuracy, specificity and F1 score. Moreover, the performance of the AGWDWT-FCM in change detection model is compared to other conventional models, and the improvement is noted.
关键词： Filter coefficient,Adaptive discrete wavelet transform,Grey wolf optimization,Synthetic aperture radar,Fuzzy C-means clustering
更新于2025-09-23 15:21:21
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Digital pulse timing with semiconductor gamma-ray detectors using a wavelet transform technique
摘要： Obtaining precise timing information from semiconductor gamma-ray detectors is of great interest for a variety of applications such as high-resolution positron emission tomography. However, pulse timing with these detectors through the common constant-fraction discrimination (CFD) method is strongly affected by the time-walk error that results from the inherent variations in the shape of the detectors’ pulses. This paper reports on the use of the wavelet transform for minimizing the time-walk error in digital CFD pulse timing with semiconductor gamma-ray detectors. The details of the method are described, and the experimental results with a 1 mm thick CdTe detector are shown. It is demonstrated that, by using the Haar wavelet transform of the digitized preampli?er pulses, the original tailed time spectrum of the detector with a time resolution of 8.22 ± 0.12 ns at full-width at half-maximum (FWHM) in the energy range of 300-550 keV improves to a symmetric time spectrum with a time resolution of 3.39 ± 0.02 ns (FWHM).
关键词： wavelet transform,time-walk error,CdTe detector,digital pulse timing,semiconductor gamma-ray detectors
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Speckle Noise Reduction of Time Series Sar Images Based on Wavelet Transform and Kalman Filter
摘要： Synthetic Aperture Radar (SAR) imaging systems can provide valuable sources of earth observation data for various applications. Speckle noise reduction of images produced by these systems is a challenging issue. In this paper, a novel method is proposed for reducing the speckle noise from time series SAR images. This method is mainly based on wavelet transform and Kalman filter. The proposed method is applied to a time series SAR images acquired by Sentinel-1 over Tehran, Iran. To demonstrate the performance of the proposed method, both qualitative and quantitative evaluations are reported compared to those of conventional speckle filtering methods. The experimental results show the good performance and efficiency of the proposed method for the speckle reduction of multitemporal SAR images. As well, the results show that the proposed method can preserve the major edge structures and the spatial resolution while reducing the time of processing.
关键词： wavelet transform,speckle noise reduction,kalman filter,Time series SAR images
更新于2025-09-23 15:21:21
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No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features
摘要： No-reference image quality assessment (NR-IQA) aims to evaluate the perceived quality of distorted images without prior knowledge of pristine version of the images. The quality score is predicted based on the features extracted from the distorted image, which needs to correlate with the mean opinion score. The prediction of an image quality score becomes a trivial task, if the noise affecting the quality of an image can be modeled. In this paper, gradient magnitude and Wiener filtered discrete wavelet coefficients are utilized for image quality assessment. In order to reconstruct an estimated noise image, Wiener filter is applied to discrete wavelet coefficients. The estimated noise image and the gradient magnitude are modeled as conditional Gaussian random variables. Joint adaptive normalization is applied to the conditional random distribution of the estimated noise image and the gradient magnitude to form a feature vector. The feature vector is used as an input to a pre-trained support vector regression model to predict the image quality score. The proposed NR-IQA is tested on five commonly used image quality assessment databases and shows better performance as compared to the existing NR-IQA techniques. The experimental results show that the proposed technique is robust and has good generalization ability. Moreover, it also shows good performance when training is performed on images from one database and testing is performed on images from another database.
关键词： Wiener filtering,Gradient magnitude,Discrete wavelet transform,No-reference image quality assessment
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Singapore, Singapore (2019.8.28-2019.8.30)] 2019 IEEE 16th International Conference on Group IV Photonics (GFP) - Towards Transparent On-Waveguide Electrical Circuits in SiN-Photonic Platform
摘要： In this paper, a 2-D noncausal Markov model is proposed for passive digital image-splicing detection. Different from the traditional Markov model, the proposed approach models an image as a 2-D noncausal signal and captures the underlying dependencies between the current node and its neighbors. The model parameters are treated as the discriminative features to differentiate the spliced images from the natural ones. We apply the model in the block discrete cosine transformation domain and the discrete Meyer wavelet transform domain, and the cross-domain features are treated as the final discriminative features for classification. The support vector machine which is the most popular classifier used in the image-splicing detection is exploited in our paper for classification. To evaluate the performance of the proposed method, all the experiments are conducted on public image-splicing detection evaluation data sets, and the experimental results have shown that the proposed approach outperforms some state-of-the-art methods.
关键词： 2-D noncausal Markov model,block discrete cosine transformation (BDCT),passive image-splicing detection,discrete Meyer wavelet transform
更新于2025-09-23 15:21:01
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[IEEE 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - New Delhi, India (2019.11.16-2019.11.17)] 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - Machine Learning Based Islanding Detection for Grid Connected Photovoltaic System
摘要： This paper focus on developing a new islanding detection method with the help of machine learning and signal processing technique. The islanding detection method make sure that there is a proper remote monitoring of the grid integrated photovoltaic (PV) system. In case of grid fault or maintained of the grid, a proper informed signal is provided to various distributed generation (DG) networks so that they can disconnect with the grid and operate in isolated mode. A simulation of 1kW grid connected PV system is performed. The signal such as voltage, current and frequency are recorded at point of common coupling (PCC). The feature of recorded signals are extracted using wavelet transformation. The extracted features are used to form a islanding scenarios matrix. The matrix is further utilized to train a classi?er using machine learning algorithm. From the result it can be observed that the trained classi?er depicted 97.9% training accuracy with a training time of 16.9 sec which is better when compared with the literature. Further the trained classi?er is subjected to test with an unknown islanding condition to observe the robustness of the classi?er.
关键词： Islanding detection,complex tree,wavelet transform,Grid integrated photovoltaic system,distribution generation
更新于2025-09-23 15:21:01
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Research and Modeling of Photovoltaic Array Channel Noise Characteristics
摘要： The photovoltaic array can be used as a medium for carrier communication to realize monitoring of photovoltaic components. Photovoltaic array channel noise, especially the pulse-type noise therein, seriously interferes carrier communication, so it is necessary to grasp the characteristics of the photovoltaic array channel noise. Photovoltaic array channel noise modeling is a key process when conducting anti-noise immunity tests of monitoring equipment. Based on the time-domain waveform of photovoltaic series channel noise which is measured in a photovoltaic power station, this paper proposes a photovoltaic array noise modeling method of Wavelet Peak-Type Markov chain, and studies the in?uence on modeling accuracy when different mother wavelets are adopted for modeling. From the simulation results, root mean square errors of the predicted output for Haar, Biorthogonal and Daubechies wavelet-based function modeling case are 0.9614 V, 1.4915 V and 0.7928 V, respectively, validating that Daubechies wavelet-based function is the best wavelet-based function of modeling. In the case that the peak of original noise reaches 20 V, the predicted mean absolute error of this model is only 0.4926 V, which not only veri?es the applicability of the Wavelet Peak-Type Markov chain model to the photovoltaic array channel noise, but also veri?es the applicability to the pulse-type noise.
关键词： photovoltaic array channel noise,noise characteristics and modeling,wavelet packet decomposition and recombination,Peak-Type Markov chain
更新于2025-09-23 15:21:01
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Performance comparison of recent optimization algorithm Jaya with particle swarm optimization for digital image watermarking in complex wavelet domain
摘要： Nowadays copyright protection is mandatory in the field of image processing to removes the illegitimate utilization and imitation of digital images. The digital image watermarking is one of the most reliable methods for protecting the illegal validation of data. In this paper, singular value decomposition based digital image watermarking scheme is proposed in complex wavelet transform (CWT) domain using intelligence algorithms like particle swarm optimization (PSO) and recently proposed Jaya algorithm. The watermark image is embedded into high frequency CWT subband of cover image. At the time of watermark embedding and extraction, optimization algorithms Jaya and PSO are applied to improve the robustness and imperceptibility by assessing the fitness function. The perceptual quality of watermarked image and robustness of extracted watermark image are verified under the filtering, rotation, scaling, Gaussian noise and JPEG compression attacks. From the comparative analysis it is proved that Jaya algorithm is better as compared to PSO algorithm under most types of attacks with higher magnitudes whereas identical under the lower magnitude of applied attacks. Moreover, using variety of cover images, it is found that, the elapse time and value of fitness function given by Jaya algorithm are also better as compared to PSO.
关键词： Particle swarm optimization,Singular value decomposition,Jaya algorithm,Complex wavelet domain watermarking,Fitness function,Elapse time
更新于2025-09-23 15:21:01
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[IEEE 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC) - Vladivostok (2018.8.18-2018.8.25)] 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC) - Image Analysis Based on Salient Points of Wavelet Transform
摘要： In the article the task of image analysis based on salient points of wavelet transform is considered. Salient points retrieval based on energy estimation of wavelet transform is described. Salient points description based on local binary patterns are proposed.
关键词： wavelet transform,segmentation,salient points,images matching
更新于2025-09-23 15:21:01