Dual-tree Complex Wavelet Transform Coefficients Research Articles

Image-Based Gating of Intravascular Ultrasound Sequences Using the Phase Information of Dual-Tree Complex Wavelet Transform Coefficients.

Intravascular ultrasound (IVUS) is a widely used interventional imaging technique for the assessment of atherosclerosis plaque. Due to pulsatile heart motions, transverse and longitudinal motions are observed during in vivo pullbacks of IVUS sequences. These motion artifacts can mislead the volume-based data retrieved from IVUS studies and hinder the visualization of the vessel condition. To overcome this problem, a new fully automatic image-based gating algorithm was proposed in the current study. We utilized the phase information of the dual-tree complex wavelet transform (DT-CWT) coefficients to detect the motion of edge-like structures. For each IVUS sequence, first, six motion signals were detected by analyzing the phase of DT-CWT coefficients in six different directions. Then, the three best motion signals were selected by analyzing the frequency properties of each signal. Subsequently, these extracted signals were filtered using a modified Butterworth band-pass filter and the gated sequence was formed by using a combination of them. The proposed method was compared to four state-of-the-art methods and its frequency spectrum had more accurate characteristics in the cardiac frequency. In addition, the gated sequence extracted by the proposed method had the highest similarity to the extracted gated sequence by the physician.

Dual tree complex wavelet transform-based signal denoising method exploiting neighbourhood dependencies and goodness-of-fit test.

A novel signal denoising method is proposed whereby goodness-of-fit (GOF) test in combination with a majority classifications-based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DT-CWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.

Robust additive watermarking in the DTCWT domain based on perceptual masking

In this paper, a robust additive image watermarking system operating in the Dual Tree Complex Wavelet Transform (DTCWT) domain is proposed. The system takes advantage of a new perceptual masking model that exploits the Human Visual System (HVS) characteristics at the embedding stage. It also uses an efficient watermark detection structure, called the Rao-test, to verify the presence of the candidate watermark. This structure relies on the statistical modeling of high frequency DTCWT coefficients by the Generalized Gaussian distribution. Experimental results show that the proposed system outperforms related state-of-the-art watermarking systems in terms of imperceptibility and robustness.

Enhancing the laws filter descriptor on DTCWT coefficients by thresholding approach for texture classification

In this paper, we propose a new approach of combining dual-tree complex wavelet transform with traditional Laws' filter descriptor for texture classification using thresholding method. The dual-tree complex wavelet transform (DTCWT) is a recent technique to discrete wavelet transform, with important additional properties. It has been observed that the thresholding is a method to keep significant information of the image while discarding the unimportant part. On this basis for further enhancement of texture classification, we have acquired the texture images by applying thresholding technique to the entire texture database. These thresholded images are then applied to the fusion model of DTCWT with Laws' filter descriptor. We verify the effectiveness of the proposed method by utilising two texture databases such as Brodatz and UIUC. The proposed methods are compared with the classical Laws' filter descriptor. Results demonstrate that the proposed method greatly enhanced the classification accuracies by using k-NN as classifier.

Fusing DTCWT and LBP Based Features for Rotation, Illumination and Scale Invariant Texture Classification

Classification of texture images with different orientation, illumination, and scale changes is a challenging problem in computer vision and pattern recognition. This paper proposes two descriptors and uses them jointly to fulfill such task. One can obtain an image pyramid by applying dual-tree complex wavelet transform (DTCWT) on the original image, and generate local binary patterns (LBP) in DTCWT domain, called LBPDTCWT, as local texture features. Moreover, log-polar (LP) transform is applied on the original image, and the energies of DTCWT coefficients on detail subbands of the LP image, called LPDTCWTE, are taken as global texture features. We fuse the two kinds of features for texture classification, and the experimental results on benchmark data sets show that our proposed method can achieve better performance than other the state-of-the-art methods.

A novel hashing scheme for Depth-image-based-rendering 3D images

Image hashing as an advanced approach has been widely applied in many areas such as image authentication, and content-based identification and retrieval. Being different from the traditional 2D image hashing scheme, virtual images which are generated in the DIBR process should be considered with the same content as the center image in Depth-image-based rendering (DIBR) 3D image hashing scheme. In this paper, we propose a hashing method for DIBR 3D images by applying three-level Dual-tree complex wavelet transform (DT-CWT) to images and selecting suitable DT-CWT coefficients to form the final hash sequence. Experimental results indicate that the proposed method is robust to a wide range of distortions and attacks. When compared with the current state-of-the-art methods, the proposed method can achieve better identification performances in terms of geometric attacks such as rotation attacks, whilst providing comparable performances under classical distortions such as additive noise, blurring, and compression. Furthermore, the proposed method is also able to ensure that the generated virtual images could be identified with the same content as the corresponding center image due to the characteristic approximate shift invariance of DT-CWT.

State detection of explosive welding structure by dual-tree complex wavelet transform based permutation entropy

Recent years, explosive welding structures have been widely used in many engineering fields. The bonding state detection of explosive welding structures is significant to prevent unscheduled failures and even catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, a new method called dual-tree complex wavelet transform based permutation entropy (DTCWT-PE) is proposed to detect bonding state of such structures. Benefiting from the complex analytical wavelet function, the dual-tree complex wavelet transform (DTCWT) has better shift invariance and reduced spectral aliasing compared with the traditional wavelet transform. All those characters are good for characterizing the vibration response signals. Furthermore, as a statistical measure, permutation entropy (PE) quantifies the complexity of non-stationary signals through phase space reconstruction, and thus it can be used as a viable tool to detect the change of bonding state. In order to more accurate identification and detection of bonding state, PE values derived from DTCWT coefficients are proposed to extract the state information from the vibration response signal of explosive welding structure, and then the extracted PE values serve as input vectors of support vector machine (SVM) to identify the bonding state of the structure. The experiments on bonding state detection of explosive welding pipes are presented to illustrate the feasibility and effectiveness of the proposed method.

Supervised image segmentation using Q-Shift Dual-Tree Complex Wavelet Transform coefficients with a texton approach

In this study, we propose a simple and efficient texture-based algorithm for image segmentation. This method constitutes computing textons and bag of words (BOWs) learned by support vector machine (SVM) classifiers. Textons are composed of local magnitude coefficients that arise from the Q-Shift Dual-Tree Complex Wavelet Transform (DT-CWT) combined with color components. In keeping with the needs of our research context, which addresses land cover mapping from remote images, we use a few small texture patches at the training stage, where other supervised methods usually train fully representative textures. We accounted for the scale and rotation invariance issue of the textons, and three different invariance transforms were evaluated on DT-CWT-based features. The largest contribution of this study is the comparison of three classification schemes in the segmentation algorithm. Specifically, we designed a new scheme that was especially competitive and that uses several classifiers, with each classifier adapted to a specific size of analysis window in texton quantification and trained on a reduced data set by random selection. This configuration allows quick SVM convergence and an easy parallelization of the SVM-bank while maintaining a high segmentation accuracy. We compare classification results with textons made using the well-known maximum response filters bank and speed up robust features features as references. We show that DT-CWT textons provide better distinguishing features in the entire set of configurations tested. Benchmarks of our different method configurations were made over two substantial textured mosaic sets, each composed of 100 grey or color mosaics made up of Brodatz or VisTex textures. Lastly, when applied to remote sensing images, our method yields good region segmentation compared to the ENVI commercial software, which demonstrates that the method could be used to generate land cover maps and is suitable for various purposes in image segmentation.

English

Research on vision-based navigation of unmanned air vehicles (UAVs) has been in focus in the recent years, both in military and civilian application domains. Vision-based navigation uses features as cues to match successive images and compute the location of the UAV. The method proposed in this paper is an extension to the existing work to automatically compute scale weights of dual-tree complex wavelet transform (DTCWT) coefficients to improve the performance in terms of detection of features as against discrete wavelets or its Fourier counterparts. Existing DTCWT coefficients give improved time-shifted sensitivity, better directional selectivity with local phase information and limited redundancy. However, the drawback of this method is that the results depend on appropriate selection of scale weights α and β, which are different for different scenes. The existing technique incorporates a rule of thumb-based recommended values of α and β, irrespective of the scene which generates too many key-points, making the registration process computationally intensive. As a real-time application, the need of the problem is to extract less number of strong features. Thus, an algorithmic approach to compute optimal values of scale weights considering precision and accuracy is proposed. The method is tested on various synthetic, simulated, and aerial images having different transformations and in the presence of noise between successive images. It is observed that DTCWT descriptor performs the best and gives better results than scale invariant feature transform and Speeded up robust features. Science Journal, Vol. 64, No. 6, November 2014, pp.549-556, DOI:http://dx.doi.org/10.14429/dsj.64.7785

A sparse representation based approach for recognition of power system transients

The present work shows how a new genre of problem solution techniques called compressive sensing (CI) can be utilized for solving power quality problems. Specifically speaking, this work undertakes the power system transient recognition problem and solves it using sparse representation based classification (SRC) technique, a popular, recently emerging technique in CI. The method employs SRC based classification in combination with a feature extraction procedure, carried out using dual tree complex wavelet transform (DTCWT). The proposed method uses statistical characteristics to extract features from the DTCWT coefficients obtained from each signal and then these extracted features are used as input arrays for the SRC based classifier. The PQ disturbance events considered in this work include four different transient conditions, namely, transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. The proposed algorithm could achieve perfect classification accuracy of the order of 100% and could comfortably outperform several similar contemporary methods known for identical power system transient classification problems.

Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage

The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS) technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT) coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding.

Score-Level Fusion of Local Spatial, Scale and Directional Features Based Face Recognition Approach for Single Sample Problem

The local appearance based methods have been successfully applied to face recognition and achieved state-of-the-art performance. In this paper we propose a face representation approach which explores the information in the spatial domain along with score-level fusion of directional features in different scales. The local spatial texture features are extracted using the local MinMax binary pattern (LMinMaxBP) approach as well as with the help of the selective local texture feature approach. The dual tree complex wavelet transform (DT-CWT) provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. It is insensitive to illumination variations and facial expression changes. 2-D dual tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail subbands are used to extract the multi scale facial features with good directional selectivity which improves the face recognition with small sample size in less computation. This score-level fusion approach combines information from different domains to give a good face representation for recognition. Extensive experimental results on FERET, ORL, Yale and Indian Face databases show the significant advantages of the proposed method over the existing ones.

An Image De-Noising Algorithm Based on the Dual-Tree Complex Wavelet

In this paper, an image de-noising algorithm based on the Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed, which takes the advantage of redundant coefficients transformed by DT-CWT. The model of bivariate shrinkage function is used to provide a nonlinear threshold strategy. It exploits the dependency between inter-scale parents and children coefficients to recover the original coefficients more accurate. With the shift-invariance property of DT-CWT coefficients, the algorithm prevents the Gibbs effect caused by the thresholding, which further improves the reconstructed quality. Experiment results show that the de-noised image using DT-CWT can achieve more than 1dB prior to DWT with impressive visual results.

A HYBRID FACE RECOGNITION APPROACH USING LOCAL FUSION OF COMPLEX DUAL-TREE WAVELET COEFFICIENTS AND RIDGELET TRANSFORM

In this paper, we propose novel face recognition method based on local appearance feature extraction using hybrid mode of local ridge-let and fused dual-tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel dis-crete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of sub-bands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail subbands and local Finite Ridgelet Transform (FRIT) coefficients of approxi-mate subbands of DT-CWT are used to extract the facial features which improve the face recognition with small sample size in less computation. The local features based methods have been success-fully applied to face recognition and achieved state-of-the-art per-formance. Normally most of the local appearance based methods the facial features are extracted from several local regions and concate-nated into an enhanced feature vector as a face descriptor. In this approach we divide the face into several (m×m) non-overlapped paral-lelogram blocks instead of square or rectangle blocks. The local mean and standard deviation of hybrid FRIT and fused DT-CWT coeffi-cients are used to describe the face image. Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local hybrid FRIT and fused DT-CWT approach performs well on illumi-nation, expression and perspective variant faces with single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our pro-posed Hybrid Local FRIT and fused DT-CWT based method has a really low computational complexity.

Generalized SAR Despeckling Based on DTCWT Exploiting Interscale and Intrascale Dependences

In this letter, dual tree complex wavelet transform (DTCWT)-based despeckling algorithm is proposed for synthetic aperture radar (SAR) images, using multivariate statistical theory. The DTCWT coefficients in each subband are modeled with a multivariate Cauchy probability density function (pdf) which takes into account the statistical dependency between the wavelet coefficients, their neighbors and coefficients across scales. Generalized expressions are derived for the dispersion parameter in the multivariate Cauchy pdf using the fractional moments and for the multivariate maximum a posteriori estimator. Experimental results show that the proposed method based on multivariate Cauchy prior achieves better performance in terms of equivalent number of looks, peak signal-to-noise ratio, and mean structural similarity index matrix.

Image denoising with anisotropic bivariate shrinkage

This paper presents a novel image denoising algorithm based on the modeling of wavelet coefficients with an anisotropic bivariate Laplacian distribution function. The anisotropic bivariate Laplacian model not only captures the child–parent dependency between wavelet coefficients, but also fits the anisotropic property of the variances of wavelet coefficients in different scales of natural images. With this statistical model, we derive a closed-form anisotropic bivariate shrinkage function in the framework of Bayesian denoising and a new image denoising approach with local marginal variance estimation based on this newly derived shrinkage function is proposed in the discrete wavelet transform (DWT) domain. The proposed anisotropic bivariate shrinkage approach is also extended to the dual-tree complex wavelet transform (DT-CWT) domain to further improve the performance of image denoising. To take full advantage of DT-CWT, a more accurate noise variance estimator is proposed and the way the anisotropic bivariate shrinkage function applied to the magnitudes of DT-CWT coefficients is presented. Experiments were carried out in both the DWT and the DT-CWT domain to validate the effectiveness of the proposed method. Using a representative set of standard test images corrupted by additive white Gaussian noise, the simulation results show that the proposed method provides promising results and is competitive with the best wavelet-based denoising results reported in the literature both in terms of peak signal-to-noise ratio (PSNR) and in visual quality.

LOCAL STATISTICAL FEATURES OF DUAL TREE COMPLEX WAVELET TRANSFORM BASED FACE RECOGNITION WITH SINGLE SAMPLE

In this paper, we propose novel face recognition method based on local appearance feature extraction using dual-tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel discrete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of subbands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The local DT-CWT coefficients are used to extract the facial features which improve the face recognition with small sample size in less computation. The local features based methods have been successfully applied to face recognition and achieved state-of-the-art performance. Normally most of the local appearance based methods the facial features are extracted from several local regions and concatenated into an enhanced feature vector as a face descriptor. In this approach we divide the face into several (m×m) non-overlapped parallelogram blocks instead of square or rectangle blocks. The local mean and standard deviation of complex wavelet coefficients are used to describe the face image.Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local DT-CWT approach performs well on illumination, expression and perspective variant faces with single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our proposed Local DT-CWT based method has a really low computational complexity.

Local fusion of complex dual-tree wavelet coefficients based face recognition for single sample problem

In this paper, we propose a novel face recognition method for the one sample problem. This approach is based on local appearance feature extraction using directional multiresolution decomposition offered by dual tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel discrete wavelet transforms (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of subbands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. The 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail subbands are used to extract the facial features which improve the face recognition with small sample size in relatively short computation time. The local features based methods have been successfully applied to face recognition and achieved state-of-the-art performance. Normally for most of the local appearance based methods, the facial features are extracted from several local regions and concatenated into an enhanced feature vector as a face descriptor. In this approach we divide the fused face representation into several (m×m) non-overlapped parallelogram blocks instead of square or rectangular blocks. Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local fusion of DT-CWT approach performs well on illumination, expression and perspective variant faces with a single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our proposed Local fusion of DT-CWT based method has a really low computational complexity.

Statistics of 2-D DT-CWT Coefficients for a Gaussian Distributed Signal

This paper deals with the statistical properties of the two-dimensional dual-tree complex wavelet transform (DT-CWT) coefficients of a Gaussian distributed signal both in the Cartesian and polar forms. The first level of decomposition of the DT-CWT uses the wavelet filters that form only an approximate Hilbert-pair, while those at the higher levels form almost an exact Hilbert-pair. Hence, a significant correlation exists between the quadrature-filtered coefficients of the two trees in the first level of decomposition as compared to the other levels. As a consequence, in the Cartesian representation, the real and imaginary components of the complex coefficients are modeled as independent zero-mean Gaussian having unequal variances for the first level of decomposition and equal variances for the higher levels. In the polar representation, the magnitude components are modeled by a generalized Gamma probability density function (PDF) for the first-level decomposition and a Rayleigh PDF for the higher levels. The corresponding phase components are modeled by an analytic PDF. The Monte Carlo simulations show that the proposed PDFs of the transform coefficients match very well with the empirical ones. It is shown that the moments of the corresponding PDFs closely approximate the estimated sample moments. Finally, two techniques, namely, maximum a posteriori-based estimation and phase-based ridge detection are developed using the proposed PDFs. Simulation studies are carried out showing that the use of the proposed techniques provides improved estimation and detection performance of images in a noisy environment.