Dual Tree Research Articles

A Multi-Objective Enhanced Fruit Fly Optimization (MO-EFOA) Framework for Despeckling SAR Images using DTCWT based Local Adaptive Thresholding

ABSTRACT The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

A GPU-accelerated fast multipole method based on barycentric Lagrange interpolation and dual tree traversal

We present a GPU-accelerated fast multipole method (FMM) called BLDTT, which uses barycentric Lagrange interpolation for the near-field and far-field approximations, and dual tree traversal to construct the interaction lists. The scheme replaces well-separated particle-particle interactions by adaptively chosen particle-cluster, cluster-particle, and cluster-cluster approximations given by barycentric Lagrange interpolation on a Chebyshev grid of proxy particles in each cluster. The BLDTT employs FMM-type upward and downward passes, although here they are adapted to interlevel polynomial interpolation. The BLDTT is kernel-independent, and the approximations have a direct sum form that efficiently maps onto GPUs, where targets provide an outer level of parallelism and sources provide an inner level of parallelism. The code uses OpenACC directives for GPU acceleration and MPI remote memory access for distributed memory parallelization. Computations are presented for different particle distributions, domains, and interaction kernels, and for unequal targets and sources. The BLDTT consistently outperforms our earlier particle-cluster barycentric Lagrange treecode (BLTC). On a single GPU for problem size ranging from N=1E5 to 1E8, the BLTC scales like O(Nlog⁡N) and the BLDTT scales like O(N). We also present MPI strong scaling results for the BLDTT and BLTC with N=64E6 particles running on 1 to 32 GPUs.

Epileptic Detection and Classification using Convolutional Neural Network with Dual Tree Complex Wavelet Features

Epilepsy is a kind of brain disease that can be diagnosed by observation of EEG signals. Mostly it occurs within the children. However, some of the cases are observed in adults. It is a challenging task for physicians to detect this disease at an early stage. Authors in this work have classified the Epileptic and normal EEG signal by adopting the deep learning approach. For efficient features, dual tree complex wavelet (DTCWT) is considered. The decomposed wavelet features are used as the input to the convolutional neural network (CNN) classifier. Around 97% classification accuracy is observed by using the proposed approach.

Performance Evolution of Face and Speech Recognition system using DTCWT and MFCC Features

Every activity in day-to-day life is required the need of mechanized automation for ensuring the security. The biometrics security system provides the automatic recognition of human by overcoming the traditional recognition methods like Password, Personal Identification Number and ID cards etc. The face recognition is a wide research with many applications. In the proposed work face recognition is carried out using DTCWT (Dual Tree Complex Wavelet Transform) integrated with predominant QFT (Quick Fourier Transform) and speech recognition is carried out using MFCC (Mel Frequency Cepstral Coefficients) algorithm. The distance formula is used for matching the test features and database features of the face and speech images. Performance variables such as EER, FRR, FAR and TSR are evaluated for person recognition

Supervised Classification Approaches for Brain Tumour Classification Using Fused Wavelet Features

In this study, an efficient pattern recognition technique is developed for Brain Image Classification (BIC) intonormal or abnormal. Wavelet transform features with supervised classification have a potential role to playin bringing Magnetic resonance Images (MRI) of the brain into practical clinical use.The developed patternrecognition technique uses Discrete Wavelet Transform (DWT), Dual tree M-band Wavelet Transform(DMWT), and Stationary Wavelet Transform (SWT) for feature extraction, k-Nearest Neighbour (kNN)and Naive Bayes (NB) for classification and is considered as an effective and accurate tool for brain imageanalysis for cancer classification. Also, the predominant coefficients are chosen from the combined featurespace by rank features of statistical feature selection approach. Results show that the proposed system actsas a pre-treatment predictor for BIC with an accuracy of 88.5% for kNN and 95.5% for NB classification.

Revisiting the Performance of DNS Queries on a DNS Hierarchy Testbed over Dual-Stack

Abstract The exponential growth of IoT devices and their need to use IPv6 addresses has the potential to create load stress on the existing DNS infrastructure and it is imperative that DNS servers to be deployed on IPv6 networks. The DNS query latency from a particular Internet vantage point for IPv4 and IPv6 network cannot be compared directly due to variations in the number of hops of query on IPv4 and IPv6 communication networks. Moreover, there is no assurance that DNS server in the hierarchy is hosted on a dual-stack. This work brings out the DNS query resolution latency over the IPv4 and IPv6 protocol stacks with better accuracy. The experiments are carried out by setting up a complete DNS hierarchy (ROOT, TLD, STLD, TTLD and recursive resolver) on dual IP stack (IPv4 and IPv6), enabling both forward and reverse lookup tree on a live testbed, ensuring a constant number of hops between the recursive resolver and each of the DNS servers in the hierarchy. This live testbed is a first of its kind and is made available for Internet researchers. The operational issues encountered during this deployment and service provisioning are discussed and documented in this paper. This paper also gives a clear illustration and provides reference guidelines for the DNS hierarchy setup, and also aims to bridge the knowledge gap required for deploying DNS over IPv6.

Wind and Solar Integrated Power Prediction Method Research Based on DT-CWT and LSTM

It is very important to improve the wind and solar integrated output power load forecasting technology with the rapid construction of wind and solar hybrid power supply system and the increasing network complexity of microgrid energy system. On the basis of traditional load power forecasting, this paper proposes a wind and solar integrated power prediction method based on dual tree complex wavelet and long short time memory network. In this method, the original power data of photovoltaic and wind power are denoised by DT-CWT. After data reconstruction and transformation, the processed data are trained by multi-port long and short time memory network prediction model, so as to achieve wind power optical integrated power prediction purpose. Finally, the field data of wind and solar hybrid power supply system is used to verify the model. The results show that the method can effectively reduce the impact of power noise during prediction, and improve the accuracy of power prediction of wind and solar hybrid power supply system.

Research on Fast Recognition Method of Complex Sorting Images Based on Deep Learning

For the logistics sorting warehouse without much light is complex, and the difference between express packaging is not obvious, a fast recognition method of sorting images based on deep learning and dual tree complex wavelet transform was studied. Sorting images are not very clear due to factors such as the enclosed environment and the weak lighting conditions of the warehouse. First, the dual tree complex wavelet transform is used to preprocess the sorting image for noise reduction and other image preprocessing. Second, a convolutional neural network (CNN) was designed. On the basis of Alexnet neural network, parameters of convolutional layer, ReLU layer and pooling layer of CNN are redefined to accelerate the learning speed of neural network. Lastly, according to the new image classification task, the last three layers of the neural network, the full connection layer, the softmax layer and the classification output layer are defined to adapt to the new image recognition. The proposed fast sorting image recognition technology based on deep learning has higher training speed and recognition accuracy in the face of more complicated sorting image recognition, which can meet the experimental requirements. Rapid identification of sorting images is of great significance to improve the efficiency of logistics in unmanned warehouses.

Replacing DWT with DTCWT in blind image rotation angle estimation

During the image tampering, rotation is often involved to make the forgery more convincing. Hence, estimating the rotation angle accurately and locally is of forensic importance. Recently, a novel rotation angle estimation scheme was proposed based on linear pattern (LP), achieving state of the art performance especially in the case when the rotation angle is small. However, due to the limitations of the involved discrete wavelet transform (DWT), the existing LP based method cannot always accurately detect rotated linear pattern from a rotated image. To fill this gap, we propose to extract the rotated LP using dual tree complex wavelet transform (DTCWT). Thanks to the good directional selectivity of DTCWT, the proposed method can extract the LP more accurately. Experiments show that our proposed method performs better than the state of the art in rotation angle estimation, and is also a promising forensic tool for tampering localization.

Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis

BackgroundFrequency band optimization improves the performance of common spatial pattern (CSP) in motor imagery (MI) tasks classification because MI-related electroencephalograms (EEGs) are highly frequency specific. Many variants of CSP algorithm divided the EEG into various sub bands and then applied CSP. However, the feature dimension of MI-EEG data increases with addition of frequency sub bands and requires efficient feature selection algorithms. The performance of CSP also depends on filtering techniques. MethodIn this study, we designed a dual tree complex wavelet transform based filter bank to filter the EEG into sub bands, instead of traditional filtering methods, which improved the spatial feature extraction efficiency. Further, after filtering EEG into different sub bands, we extracted spatial features from each sub band using CSP and optimized them by a proposed supervised learning framework based on neighbourhood component analysis (NCA). Subsequently, a support vector machine (SVM) is trained to perform classification. ResultsAn experimental study, conducted on two datasets (BCI Competition IV (Dataset 2b), and BCI competition III (Dataset IIIa)), validated the MI classification effectiveness of the proposed method in comparison with standard algorithms such as CSP, Filter bank CSP (CSP), and Discriminative FBCSP (DFBCSP). The average classification accuracy obtained by the proposed method for BCI Competition IV (Dataset 2b), and BCI Competition III (Dataset IIIa) are 84.02 ± 12.2 and 89.1 ± 7.50, respectively and found significant than that achieved by standard methods. ConclusionAchieved superior results suggest that the proposed algorithm can improve the performance of MI-based Brain-computer interface devices.

An efficient image segmentation and classification of lung lesions in pet and CT image fusion using DTWT incorporated SVM

Automatic segmentation and classification of fused lung Computed Tomography (CT) and Positron Emission Tomography (PET) images is presented. This system consists of four basic stages: 1). Lung image fusion process; 2). Segmentation of fused lung CT/PET images; 3). Post pre-processing; 4). Classification of fused lung images. In the first step, the lung image fusion process is made by deep learning method. At first, the input CT/PET images are decomposed by Dual Tree m-band Wavelet Transform (DTWT). The coefficients of DTWT are fused by deep learning method. This fused image of CT/PET is the input for the following steps. In the next step, the fused CT/PET images are decomposed by DTWT. It produces lower and higher frequency sub-band coefficients. Then the lower frequency components are set to zero. Then higher frequency components are used for reconstruction. Then the clustering-based thresholding method is used for segmentation. In post pre-processing step, the unwanted small regions are removed by morphological operations. Then the lung region is detected. At last, in the classification step, the features are extracted by the intensity and texture-based features. These features are classified by hybrid classifiers like Support Vector Machine (SVM) are used. The performance of the system has a higher classification accuracy of 99% using SVM classifier.

Hybrid Vessel Extraction Method Based on Tight-Frame and EM Algorithms by Using 2D Dual Tree Complex Wavelet

Hybrid Vessel Extraction Method Based on Tight-Frame and EM Algorithms by Using 2D Dual Tree Complex Wavelet

An optimised clustering algorithm with dual tree DS for lossless image compression

The emerging utilisation of web and other electronic applications have expedited much consideration on image compression systems to spare storage room and diminish transmission time by compressing the size of an image by discarding the repetitive data sequences. Most of the techniques are based on lossy compression techniques where the compression ratio would be low. The proposed system based on lossless compression technique achieves best compression ratio, good image quality and less PSNR value by extracting the best features of an image, which is to be compressed and encoded by incorporating firefly algorithm with k means algorithm there by avoids local optima problem. To make the eminent compression of best derived features, quad tree decomposition and Huffman encoding technique are combined, which provide high compression ratio by fetching correct probabilities of occurrence of pixel intensity. This proposed technique is actualised in MATLAB and in this manner the trial results demonstrated the effectiveness of the proposed image compression technique regarding high compression ratio, low noise ratio, and reduced compression and decompression time when compared with existing techniques.

An optimised clustering algorithm with dual tree DS for lossless image compression

An optimised clustering algorithm with dual tree DS for lossless image compression

An improved iris recognition system based on the fusion of the curvelet and DTCWT

In recent years, iris recognition has been emerged as one of the most popular biometric techniques because it guarantees high universality, distinctiveness, permanence, collectability, performance, acceptability, circumvention. In the paper we propose an improved system for iris recognition with high accuracy by fusing curvelet and dual tree complex wavelet transform. In our system, the main features are extracted from pre-processed/normalized iris images using both curvelet and Dual Tree Complex Wavelet Transform (DTCWT) tranforms. After performing different classifiers independently, all the results are fused to get final classification in the decision level to increase the accuracy of system. Finally, the random forest classifier and CATIA dataset are used to measure the performance of the proposed method. The experimental results show that the technique of the paper based on fusion of the curvelet and DTCWT is promising when compared with other existing similar techniques.

Dual Fast Marching Tree Algorithm for Human-Like Motion Planning of Anthropomorphic Arms With Task Constraints

This article presents a dual fast marching tree algorithm that consists of constrained fast marching tree planning in a Cartesian space (C_FMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> ) and human-like fast marching tree planning in self-motion manifolds (H_FMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> ) for human-like motion planning for anthropomorphic arms with task constraints. The key idea of the proposal is to exploit dual sampling in a Cartesian space and self-motion manifolds to explore the entire configuration space. The C_FMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> reduces the constrained planning problem to the unconstrained instance by sampling in the obstacle-free Cartesian space and satisfying the task constraints; it can solve most of these constrained path planning tasks quickly and obtain lower cost solutions compared to the existing techniques. In addition, a validity checking method of Cartesian sampling points based on self-motion manifolds is introduced to ensure the probabilistic completeness of the new planner. By analyzing musculoskeletal models of the human arm and the muscle strength property, a torque effort criterion was deduced to generate biomimetic motion for anthropomorphic arms. Then, an H_FMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> that incorporates the FMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> algorithm with the torque effort criterion is also proposed and used to bias the tree growth toward human-like movements in the self-motion manifolds of the obtained path. Finally, the proposed approach has been illustrated with several real examples executed with a humanoid robot. The obtained results show that the paths obtained with the proposed approach are quicker and more human-like.

A novel Watermarking Technique Based on Hybrid Transforms

This paper proposed Anovel watermarking scheme using hybrid of Dual Tree Complex Wavelet Transform and singular value decomposition . Image watermarking is to embed copyright data in image bit streams. Our proposed technique demonestrates the effective and robust of image watermarking algorithms using a hybrid of two strong mathematical transforms; the 2-level Dual Tree Complex Wavelet Transform (DT-CWT) and Singular Value Decomposition (SVD). This technique shows high level of security and robustness against attacks. The algorithm was tested for imperceptibility and robustness and the results were compared with DWT-SVD-based technique, it is shown that the proposed watermarking schemes is considerably more robust and effective.

Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain

The fungal diseases in banana cause major yield losses for millions of farmers around the globe. Early detection of these diseases helps the farmers to devise successful management strategies. The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease. This paper demonstrates a methodology for classification of three important foliar diseases in banana, using local texture features. The disease affected regions are identified using image enhancement and color segmentation. Segmented images are converted to transform domain using three image transforms (DWT, DTCWT and Ranklet transform). Feature vector is extracted from transform domain images using LBP and its variants (ELBP, MeanELBP and MedianELBP). These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure. Experimental results showed best classification performance for ELBP features extracted from DTCWT domain (accuracy 95.4%, precision 93.2%, sensitivity 93.0%, Fscore 93.0% and specificity 96.4%). Compared with traditional methods of feature extraction, this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.

Ultra-High Frequency Partial Discharge Signal Recognition in GIS based on Fisher Linear Discriminant Theory

In this paper, dual tree complex wavelet transform (DT-CWT) is used to decompose UHF partial discharge (PD) signal in multi-scale. The optimal decomposition level of complex wavelet is solved. The wavelet energy features of real part and imaginary part of UHF-PD signal under the optimal decomposition scale are extracted. Fisher linear discriminant method is used to select the features of energy features and to identify the PD types. The results show that the optimized real part and imaginary part high frequency wavelet energy features can effectively identify four kinds of typical insulation defects, and the recognition rate can reach to 92.5%. Meanwhile, the optimal complex wavelet energy (OCWEF) feature has better sensitivity and recognition effect in PD type identification.

Water leak location based on improved dual-tree complex wavelet transform with soft thresholding de-noising

Water leakage control emerges as a prime concern among researchers and water utility companies due to ever-increasing water loss level. Acoustic leak detection technique is a promising and widely used approach. Unfavorably, the deficiency of this method is that acoustic waves are interfered by undesirable noise and presence of multi-modal dispersive wave. The conventional correlation-based method assumes leak noise propagates as a single non-dispersive wave and this leads to unreliable detection method. This paper concerns on noise reduction through an improved de-noising method and leak localization by considering wave dispersion. Wavelet de-noising is a common de-noising method used in past leak detection works. But it has shortcomings of frequency aliasing and shift-variant due to DWT decomposition. Therefore, a shift-invariant Dual Tree Complex Wavelet Transform (DTCWT) is introduced here to substitute DWT for wavelet de-noising. DTCWT will decompose the signal into several bandwidths then soft thresholding is applied to remove noise by eliminating the irrelevant signals. Multilevel DTCWT decomposition aids to tackle the problem encountered by basic localization method because the wave velocity is evaluated based on the dominant frequency and dispersion curve. Experimental results show that the proposed de-noising method outperforms ordinary wavelet de-noising. Besides, the method effectively removes noise and makes the peak of cross-correlation function more pronounced and hence it is achievable to increase leak localization accuracy. The proposed method outweighs other methods by offering the lowest false trips occurrence and minimal localization error of 3.33%. A field test result proved that the method is able to locate the leak in a 235-meter-long, underground buried pipe with a minimal error of 2.63% which is 57% lower than the conventional method.