Wavelet Coefficients In Subband Research Articles

Statistical Characterization of Seismic Signals

This paper presents a study on the applicability of a method for the statistical characterization of seismic signals which is based on the statistics of their wavelet sub-band coefficients using [Formula: see text] stable distributions. The method was originally applied to underwater acoustic signals of the type used in ocean acoustic tomography and seabed classification applications. The same protocol was applied to seismic signals representing the vertical displacement, measured on a traditional seismograph. The study showed that this process can indeed provide a means of characterizing a seismic signal and be used to estimate similarities between seismic signals, which is an important factor in studying seismic activity in an area.

Secure sparse watermarking on DWT-SVD for digital images

This paper suggests a sparse watermark image embedding scheme on the singular values of the pre-selected wavelet sub-band coefficients of the digital images. Dictionary learning (DL) method is used to make the watermark image sparse. Then a watermark decoder is designed based on the theory of compressed sensing (CS) in the framework of alternating direction method of multiplier (ADMM). The integration of DL and CS offers two benefits, firstly DL combined with singular value decomposition (SVD), enables protection against false positive detection (FPD) where the sparse watermark enhances embedding capacity at high imperceptibility of the hidden data. Simulation results show high visual quality for the watermarked images (PSNR = 60.45 dB, SSIM = 0.9997 and FSIM = 0.9979) and robustness (NCC value for the decoded watermark is ∼ 0.9) against various image processing operations including an unauthorized decoding - a typical value of 85% CS measurements lead to a watermarked image results in a FPD value of 0.42 that shows a visually indistinguishable watermark.

Synovial Sarcoma Classification Technique Using Support Vector Machine and Structure Features

Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology. The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures. Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications. A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs). This framework used the SS images, and it has been transformed into frequency sub-bands using Discrete Wavelet Transform (DWT). The sub-band wavelet coefficients of SSC and other Structure Features (SF) are extracted using Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA) techniques. Here, the maximum and minimum margin between hyperplane values of the kernel parameters are adjusted periodically as a result of storing the SF values of the SVs in the IoT devices. The performance characteristics of internal cross-validation and its statistical properties are evaluated by cross-entropy measures and compared by nonparametric Mann-Whitney U test. The significant differences in classification performance between the techniques are analyzed using the receiver operating characteristics (ROC) curve. The combination of QDA + SVM technique will be required for intelligent cancer diagnosis in the future, and it gives reduced statistic parameter feature set with greater classification accuracy. The IoT network based QDA + SVM classification technique has led to the improvement of SS cancer prognosis in medical industry applications using blockchain technology.

Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal

An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process. Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures. However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity. Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis. Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band. These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients. To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures. Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysis.

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

Hyperspectral imaging is known for its rich spatial–spectral information. The spectral bands provide the ability to distinguish substances spectra which are substantial for analyzing materials. However, high-dimensional data volume of hyperspectral images is problematic for data storage. In this paper, we present a lossy hyperspectral image compression system based on the regression of 3D wavelet coefficients. The 3D wavelet transform is applied to sparsely represent the hyperspectral images (HSI). A support vector machine regression is then applied on wavelet details and provides vector supports and weights which represent wavelet texture features. To achieve the best possible overall rate-distortion performance after regression, entropy encoding based on run-length encoding and arithmetic encoding is used. To preserve the spatial pertinent information of the image, the lowest sub-band wavelet coefficients are furthermore encoded by a lossless coding with differential pulse code modulation. Spectral and spatial redundancies are thus substantially reduced. Experimental tests are performed over several HSI from airborne and spaceborne sensors and compared with the main existing algorithms. The obtained results show that the proposed compression method has high performances in terms of rate distortion and spectral fidelity. Indeed, high PSNRs and classification accuracies, which could exceed 40.65 dB and $$75.8\%$$, respectively, are observed for all decoded HSI images and overpass those given by many cited famous methods. In addition, the evaluation of detection and compression over various bands shows that spectral information is preserved using our compression method.

The value of the standard deviation of wavelet subband coefficients as feature extraction for electro encephalo graph (EEG) signal

In principle, this study describes the classification of EEG signals using Backpropagation Neural Network as a classification and Discrete Wavelet Transformation for feature extraction by taking the standard deviation value on each Wavelet subband. The purpose of this research was to identify the EEG signals used in the movement of the cursor. BCI competition 2003 data sets Ia as the data for this research. This data contains the data classes 0 (for the movement of the cursor to the top) and class 1 (for the movement of the cursor to the bottom). EEG signals are classified in two stages. In the first stage, the value of the standard deviation on each discrete wavelet subband used to extract the features of EEG signal data. This feature as inputs on Back propagation Neural Network. On the second stage of the process into two classes (class 0 and class 1) EEG signal data file, there are 260 training data file 293 of EEG and signal data file signals EEG testing, so that the whole be 553 file data signals EEG. The results obtained for the EEG signal classification was 78.7% of the data signal is tested.

No-reference image quality assessment based on AdaBoost BP neural network in wavelet domain

Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment (NR-IQA) method based on the AdaBoost BP Neural Network in Wavelet domain (WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics (NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the LIVE database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence for the database and the relatively high operation efficiency of this method.

Statistical Characterization of Acoustic Signals Using 1D Wavelet Transforms with Applications in Acoustical Oceanography

The paper summarizes the research carried out at the University of Crete and the Foundation for Research and Technology-HELLAS aiming at the statistical characterization of underwater acoustic signals and their subsequent use for geoacoustic inversions and applications in ocean acoustic tomography. In these applications, an acoustic signal recorded in the marine environment due to some source is used as the carrier of information on the physical parameters of the environment. Statistical characterization of acoustic signals is a pre-processing technique aiming at the definition of signal observables, to be used as input data of appropriately defined inverse problems aiming at the estimation of critical parameters of the marine environment. The statistical characterization scheme was introduced as a way to define signal observables especially in cases that typical observables such as ray arrivals or modal arrivals cannot be identified in the recorded signals. Moreover, the setting of the associated inverse problem requires just a single recording device, which renders its application practically and relatively cheap in comparison with signal inversion methods requiring reception at an array of hydrophones. The characterization scheme is based on a wavelet transform of the signal at various levels, followed by the statistical description of the wavelet sub-band coefficients. It is shown that A-stable symmetric distributions are capable of defining the statistics of these coefficients, the characteristic parameters of which are the observables of the signal to be exploited for the inversions. As the inverse problems associated with the sought applications are formulated as optimization processes, an objective function to be used as a similarity measure is defined, which in the case of the statistical characterization method is the Kullback–Leibrer Divergence (KLD), capable of comparing probability density functions. The inversion processes are performed by means of neural networks or genetic algorithms, and the performance of the combined signal characterization and inversion method has been tested with simulated and real data. It is shown, that the method works well especially with noise-free or denoised signals.

Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

Classification of acoustic and seismic signals based on the statistics of their wavelet sub-band coefficients

Classification of acoustic and seismic signals based on the statistics of their wavelet sub-band coefficients

Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy—Preliminary results

PurposeTo test in a first proof-of-concept study whether texture analysis (TA) allows for the detection of myocardial tissue alterations in hypertrophic cardiomyopathy (HCM) on non-contrast T1-weighted cardiac magnetic resonance (CMR) images using machine learning based approaches. MethodsThis retrospective, IRB-approved study included 32 patients with known HCM. Thirty patients with normal CMR served as controls. Regions-of-interest for TA encompassing the left ventricle were drawn on short-axis non-contrast T1-weighted images using a freely available software package. Step-wise dimension reduction and texture feature selection was performed for selecting features enabling the detection of myocardial tissue alterations in HCM patients on non-contrast T1-weighted CMR images. ResultsComparing HCM patients and controls, four texture features were identified showing significant differences between groups (Grey-level Non-uniformity [GLevNonU]: 74 ± 17 vs. 38 ± 9, p < .001; Energy of wavelet coefficients in low-frequency sub-bands [WavEnLL]: 58 ± 5 vs. 48 ± 10, p < .001; Fraction: 0.70 ± 0.07 vs. 0.78 ± 0.05, p < .001; Sum Average: 16.6 ± 0.4 vs. 17.0 ± 0.5, p = .007). A model containing the single parameter GLevNonU proved to be the best for differentiating between HCM patients and controls with a sensitivity/specificity of 91%/93%. A cut-off of GLevNonU ≥46 allowed for distinguishing HCM patients from controls with a sensitivity/specificity of 94%/90%. Even in patients without late gadolinium enhancement (LGE), the defined cut-off led to a differentiation of LGE- patients from healthy controls with 100% sensitivity and 90% specificity. ConclusionsTA on non-contrast T1-weighted images allows for the detection of myocardial tissue alterations in the setting of HCM with excellent accuracy, delivering potential novel parameters for a non-contrast assessment of myocardial texture alterations.

Convolution implementation with a novel approach of DGHM multiwavelet image transform

Abstract The purpose of this paper is to develop convolution implementation of DGHM (Donovan, Geronimo, Harding, Massopust) multiwavelet image transform using a new approach of ordering wavelet coefficients at the second and higher levels. Firstly, the method of implementation of one-dimensional discrete multiwavelet transform (1D DMWT) for DGHM multiwavelet using discrete convolution and scalar filters is presented. Then, convolution implementation of DGHM multiwavelet image transform by application of 1D DMWT for two dimensions (2D) in a separable way is proposed. Next, the second level of 2D DMWT is performed in three possible ways. The novelty of the proposed implementation is in reordering of L subband wavelet coefficients at the first level into one subimage. The results are evaluated as the energy ratios between the transformed images in L subband at the second level and the input original image. According to the experimental results, the developed implementation of 2D DMWT is approximately 5% more effective in energy compression than the ones most commonly mentioned in the literature. This paper shows a possibility of convolution implementation of 2D DMWT with higher energy compression.

A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis

A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.

VIDEO DENOISING BASED ON EXPLOITING INTRASCALE AND INTERSCALE DEPENDENCY IN WAVELET DOMAIN

In this paper, we propose a new video denoising algorithm which uses an efficient wavelet based spatio-temporal filter. The filter first applies 2D discrete wavelet transform (DWT) in horizontal and vertical directions on an input noisy video frame and then applies 1-D discrete cosine transform (DCT) in the temporal direction in order to reduce the redundancies which exist among the wavelet coefficients in the temporal direction. We observe that the subband coefficients with large magnitudes occur in clusters in locations corresponding to the edge locations even after applying the above spatiotemporal filter. In this paper, we propose to use two different low complexity wavelet shrinkage based methods to denoise the noisy wavelet coefficients in different subbands. The first method exploits the intra-scale dependencies between the coefficients and thresholds the wavelet coefficients based on the measure of sum of squares of all wavelet coefficients within a square neighborhood window. The second method exploits the inter-scale dependencies between the coefficients at different scales in an individual slice of coefficients. After filtering the individual slices of coefficients, the denoised video frames in time domain are obtained after inverse transforms. We propose to exploit the temporal redundancies between the successive frames again in the time domain using low complexity selective recursive temporal filtering (SRTF). In the proposed video denoising scheme, since the temporal redundancy is exploited both in the time and wavelet domain, the denoising capability of the scheme is hence increased. The video denoising performance using the two proposed approaches outperform many existing well known video denoising techniques including one recent well known method which uses the similar transformation, both in terms of PSNR and visual quality. We also show that, simple soft thresholding using Donoho’s threshold when used with this wavelet based spatio-temporal filter even outperforms many well known non linear based video denoising techniques.

A new feature extraction scheme in wavelet transform for stego image classification

In steganalysis, there exist a good number of feature extraction techniques; the most commonly used feature extraction technique is based on the Discrete Wavelet Transform (DWT). Almost all of the techniques give a higher detection rate even for short messages, but they are limited to statistical moments such as Characteristic Function (CF) and Probability Density Function (PDF) moments to build their feature sets. A new feature extraction scheme in wavelet transform is presented in this paper. It is based on the adaptation of Zipf’s law to wavelet transform to extract new features obtained from the statistical distributions of wavelet pattern subbands that are represented by a graphical representation called a curve of Zipf. The originality of this work can be shown through two perspectives: the adaptation of Zipf’s law to wavelet subbands, which involves the determination of the pattern size used to count the frequency of pattern appearance, and an encoding model, which makes the distribution of pattern frequencies more significant. Moreover, this novel scheme proposes a new feature set extracted from the curve of Zipf of the wavelet subband coefficients, which is the first such attempt in DWT and the field of steganalysis. A Random Forest classifier (RF) is then used to model the resulting features. Experimental results show that the proposed steganalytic method has the best classification performance in terms of accuracy, which achieved 88.9, 78.25, 84.27 and 60.1%, compared with three feature sets based on CF moments and Markov features, histogram features, and co-occurrence matrix features, respectively.

A study on vulnerability and presentation attack detection in palmprint verification system

As biometric systems become ubiquitous in the domain of personal authentication, it is of utmost importance that these systems are secured against attacks. Among various types of attacks on biometric systems, the presentation attack, which involves presenting a fake copy (artefact) of the real biometric to the biometric sensor to gain illegitimate access, is the most common one. Despite the serious threat posed by these attacks, not much work has been done to address this vulnerability in palmprint-based biometric systems. This paper demonstrates the vulnerability of a palmprint verification system to presentation attacks and proposes a novel presentation attack detection (PAD) approach to discriminating between real biometric samples and artefacts. The proposed PAD approach is inspired by a work that established relationship between the surface reflectance and a set of statistical features extracted from the image. Specifically, statistical features computed from the distributions of pixel intensities, sub-band wavelet coefficients and the grey-level co-occurrence matrix form the original feature set, and CFS-based feature selection approach selects the most discriminating feature subset. A trained binary classifier utilizes the selected feature subset to determine whether the acquired image is of real hand or an artefact. For performance evaluation, an antispoofing database—PALMspoof has been developed. This database comprises left- and right-hand images of 104 subjects, and three kinds of artefacts generated from these images. In addition to PALMspoof database, the biometric system’s vulnerability has been assessed on display and print artefacts generated from two publicly available palmprint datasets. Our experimental results show that 1) the palmprint verification system is highly vulnerable with spoof acceptance of 84.56%; 2) the proposed PAD approach is effective against both print and display attacks, in both same-device and cross-device scenarios; and 3) the proposed approach for PAD provides an average improvement of 12.73 percentage points in classification error rate over local binary pattern (LBP)-based PAD approach.

A new locally optimum watermark detection using vector-based hidden Markov model in wavelet domain

Watermark detection is a way of verifying the existence of a watermark in a watermarking scheme used for copyright protection of digital data. Statistical modeling of wavelet subband coefficients has been extensively used in watermark detection. The effectiveness of a watermarking scheme depends directly on how the wavelet coefficients are modeled. It is known that the vector-based hidden Markov model (HMM) is a very powerful statistical model for describing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross orientation dependencies of the wavelet coefficients. In this paper, it is shown that modeling using the vector-based HMM gives a better fit for the empirical data in comparison to modeling with Cauchy, Bessel-K form (BKF) and generalized Gaussian (GG) distributions. In view of this, we propose a locally-optimum blind watermark detector using the vector-based HMM in the wavelet domain. In a Bayesian framework, closed-form expressions for the mean and variance of a test statistic are derived, experimentally validated and used in evaluating the performance of the proposed detector. Using a number of test images, the performance of the proposed detector is evaluated. It is shown that the proposed detector provides a detection rate higher than that provided by other detectors designed based on the Cauchy, Gaussian, BKF or GG distributions for the wavelet coefficients. The proposed detector is also shown to be highly robust against various kinds of attacks.

Denoising Underwater Acoustic Signals for Applications in Acoustical Oceanography

A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.

Data hiding using lifting scheme and genetic algorithm

In this paper, data hiding algorithm by using lifting scheme and genetic algorithm (GA) has been proposed. Arnold transform has been used to scramble the secret image to secure the extraction of secret image. Lifting scheme is applied on the cover image to get the wavelet subbands. In this algorithm, scrambled secret image is embedded into significant wavelet coefficients of subbands of cover image. Scaling factor (SF) parameter is used in embedding and extracting process of the proposed algorithm and GA is used to optimise this parameter. This optimisation is used to maximise the value of peak signal to noise ratio (PSNR) of composite image and similarity index modulation (SIM) of extracted secret image. Experimental results reveal that proposed algorithm provides high embedding capacity and better quality of composite images than the existing data hiding techniques. To show the effectiveness of the proposed algorithm, statistical tests have been performed to show that the imperceptibility is maintained.

Data hiding using lifting scheme and genetic algorithm

In this paper, data hiding algorithm by using lifting scheme and genetic algorithm (GA) has been proposed. Arnold transform has been used to scramble the secret image to secure the extraction of secret image. Lifting scheme is applied on the cover image to get the wavelet subbands. In this algorithm, scrambled secret image is embedded into significant wavelet coefficients of subbands of cover image. Scaling factor (SF) parameter is used in embedding and extracting process of the proposed algorithm and GA is used to optimise this parameter. This optimisation is used to maximise the value of peak signal to noise ratio (PSNR) of composite image and similarity index modulation (SIM) of extracted secret image. Experimental results reveal that proposed algorithm provides high embedding capacity and better quality of composite images than the existing data hiding techniques. To show the effectiveness of the proposed algorithm, statistical tests have been performed to show that the imperceptibility is maintained.