Wavelet Filter Research Articles

Satellite image fusion using undecimated rotated wavelet transform

This paper presents two satellite image fusion algorithms namely decimated/subsampled rotated wavelet transform (SSRWT) and undecimated/non-subsampled rotated wavelet transform (NSRWT) using 2D rotated wavelet filters for extracting relevant and pragmatic information from MS and PAN images. Three major visual artefacts such as colour distortion, shifting effects and shift distortion are identified in the fused images obtained using SSRWT which are addressed by using NSRWT. The proposed NSRWT algorithm preserves spatial and spectral features of the source MS and PAN images resulting fused image with better fusion performance. The final fused image provides richer information (in terms of spatial and spectral quality) than that of the original input images. The experimental results strongly reveal that undecimated fusion algorithm (NSRWT) not only performs better than decimated fusion algorithm (SSRWT) but also improves spatial and spectral quality of the fused images.

Complex technique for wavelet filtering of pulse wave signal

Complex technique for wavelet filtering of pulse wave signal

Methods of discrete wavelet filtering of measuring signals: an algorithm for choosing a method

Methods of discrete wavelet filtering of measuring signals: an algorithm for choosing a method

POTENTIALITY OF USING DIGITAL WAVELET/QMF PYRAMIDS IN REMOTELY SENSED SATELLITES’ IMAGES CLASSIFICATION

Wavelet or quadrature mirror filter (QMF) satellites’ images are not commonly used in classification because of the modification in spectral responses that may confuse any classifier. Boundary pixels are hardly classified correctly in pixel-based classification especially in medium and coarse resolution. In such case, the sudden change in landcover is not measurable by the classifiers because the pixel may contain mor than one class. This research work is a trial to investigate the proper enhancement in accuracy that may occur by using wavelet/QMF bands’ pyramids are in classification instead of the original image bands. The reference map is prepared traditionally to measure the performance of the new system. The Wavelet/QMF image is constructed for each band of the satellite image. Then the classification is carried out for both the Wavelet/QMF image pyramid and the original satellite image using competitive learning neural networks (CLNN) method. The evaluation is carried out by comparing the classified Wavelet/QMF image with the classified original image. A statistical test is carried out to study the significance of using the classified Wavelet/ QMF image in classification.

Using Some Wavelet Shrinkage Techniques and Robust Methods to Estimate the Generalized Additive Model Parameters in Non-Linear Models

In this paper, the method of estimating the Generalized Additive Models (GAM) was highlighted, and a proposed robust weighted composition was found by combining the robust M method with the smoothing splines to estimate the Robust Generalized Additive Model and its notation is (RGAM). This estimator is used to deal with the effect of the presence of outliers in the data that do not fit into the overall data pattern by relying on some of the weight functions of the robust M method. Wavelet Shrinkage technique is used as well, which has been proposed as a smoothing of data using several types of wavelet filters in calculating the discrete wavelet transformation and relying on it in estimating the wavelet generalized additive model symbolized by (WGAM). In the case of using the simulation method, when data is contaminated with distributions ((t) Dis., Exp. Dis.) And with contamination rates (5%, 15%, 35%) and with sample sizes (50,150,300) it is noted that the smoothing method is with the Bisequare weight (BRGAM). It had a better performance compared to the rest of the methods for the simulated scenarios covered. The GCV criterion showed a marked advantage over other criteria, especially when estimating the model in the proposed robust M (RGAM) model. Some statistical criteria have been adopted. These criteria of the Generalized Additive Model (GAM) is used to compare estimation methods, the proposed methods were tested on simulation experiments as well as on real data collected from Ibn Sina Learning Hospital on cases of short stature, and the RGAM method gave the best results compared to the ordinary GAM and WGAM methods, and that by obtaining the smallest GCV value, this is because it is responsible for selecting the most suitable smoothing parameter for the smoothing spline estimator.

Differential confocal measurement for surface topography with microstructures based on spiral scanning and wavelet filter.

This paper proposes a novel spiral-scanning laser differential confocal measurement method (SSLDCM) for fast and precise measurement of surface topography with microstructures. Spiral plane scanning is used to eliminate frequent acceleration and deceleration problems in traditional raster-scanning differential confocal measurement systems and helps to keep the measuring process efficient and stable. To solve the problem of uneven sampling distribution during spiral scanning, a variable sampling rate method is adopted to distribute the sampling points at equal intervals, which would help to reduce the time of the 3D imaging process. A denoising method based on an adaptive wavelet threshold is proposed to filter the existing noise during the measuring process. An experimental measurement platform based on SSLDCM is constructed, and the axial response curve is tested and analyzed. The linear region range of the experimental platform reaches 13 µm, and the slope is about 164.15 mV/µm. In addition, the measurement results of a silicon wafer specimen by SSLDCM show good consistency with a commercial high-precision microscope, and the largest deviation is less than 2.71%. The SSLDCM has great potential to be used in various noncontact surface measurement applications with high efficiency and accuracy.

The Empirical Watershed Wavelet.

The empirical wavelet transform is an adaptive multi-resolution analysis tool based on the idea of building filters on a data-driven partition of the Fourier domain. However, existing 2D extensions are constrained by the shape of the detected partitioning. In this paper, we provide theoretical results that permits us to build 2D empirical wavelet filters based on an arbitrary partitioning of the frequency domain. We also propose an algorithm to detect such partitioning from an image spectrum by combining a scale-space representation to estimate the position of dominant harmonic modes and a watershed transform to find the boundaries of the different supports making the expected partition. This whole process allows us to define the empirical watershed wavelet transform. We illustrate the effectiveness and the advantages of such adaptive transform, first visually on toy images, and next on both unsupervised texture segmentation and image deconvolution applications.

Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors

This paper proposes and evaluates the LFrWF, a novel lifting-based architecture to compute the discrete wavelet transform (DWT) of images using the fractional wavelet filter (FrWF). In order to reduce the memory requirement of the proposed architecture, only one image line is read into a buffer at a time. Aside from an LFrWF version with multipliers, i.e., the LFr WF m , we develop a multiplier-less LFrWF version, i.e., the LFr WF ml , which reduces the critical path delay (CPD) to the delay T a of an adder. The proposed LFr WF m and LFr WF ml architectures are compared in terms of the required adders, multipliers, memory, and critical path delay with state-of-the-art DWT architectures. Moreover, the proposed LFr WF m and LFr WF ml architectures, along with the state-of-the-art FrWF architectures (with multipliers (Fr WF m ) and without multipliers (Fr WF ml )) are compared through implementation on the same FPGA board. The LFr WF m requires 22% less look-up tables (LUT), 34% less flip-flops (FF), and 50% less compute cycles (CC) and consumes 65% less energy than the Fr WF m . Also, the proposed LFr WF ml architecture requires 50% less CC and consumes 43% less energy than the Fr WF ml . Thus, the proposed LFr WF m and LFr WF ml architectures appear suitable for computing the DWT of images on wearable sensors.

Optimal Deep Belief Network with Opposition based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach

Background and ObjectiveThis research proposes a successful method of extracting Gray-Level Co-occurrence Matrix (GLCM) picture handling models to classify low-and high-metastatic cancer organisms with five prevalent cancer cell line pairs, coupled with the scanning laser picture projection technique and the typical textural function, i.e. contrast, correlation, power, temperature and homogeneity. The most significant level of disease for highly metastatic cancer cells are the degree of disturbance, contrast as well as entropy refers to the energy and homogeneity. A texture classification scheme to quantify the emphysema in Computed Tomography (CT) pictures is performed. Local binary models (LBP) are used to characterize areas of concern as texture characteristics and intensity histograms. A wavelet filter is used to acquire the informative matrix of each picture and decrease the dimensionality of the function space in the suggested method. A four-layer profound creed network is also used to obtain characteristics of elevated stage. Local Tangent Space Alignment (LTSA) is then used to compress the multi-domain defect characteristics into low dimensional vectors as a dimension reduction method. An unmonitored deep-belief network (DBN) is intended for the second phase to learn the unmarked characteristics. The strategy suggested uses Opposition Based Teaching (OBL), Position Clamping (PC) and the Cauchy Mutation (CM) to improve the fundamental PBA efficiency. MethodsThis research presents a fresh meta-heuristic algorithm called Opposition-Based Pity Beetle Algorithm (OPBA), which assesses effectiveness against state-of-the-art algorithms. OBL speeds up the convergence of the technique as both PC and CM assist OPBA with escaping local optima. The suggested algorithm was motivated by the behaviour of the beetle, which had been named six-toothed spruce bark beetle to aggregate nests and meals. This beetle can be found and harvested from weakened trees ' bark in a forest, while its populace can also infest healthy and robust trees when it exceeds the specified threshold. Results & ConclusionThe methodology has been evaluated on CT imagery from the Lung Image Database Consortium and Image Resources Initiative (LIDC-IDRI), with a maximum sensitivity of 96.86%, precision of 97.24%, and an accuracy of 97.92%.

A novel pooling layer based on gaussian function with wavelet transform

<p>Convolution represent basic layer in the convolutional neural network, but it<br />can result in big size of the data, which may increase the complexity of the<br />network. Different pooling methods are used to perform down sample these<br />data. In this paper, we have proposed a novel pooling method by using<br />Gaussian function to determine the wavelet filter coefficients . At first, the<br />basic statistics are determined for each pool size of the signal, then Gaussian<br />probability distribution function is determined . According to the procedure<br />of extracting the features , three methods are proposed , the first method is<br />used the normalized values of basic statistics as wavelet filter to be<br />multiplied by original signal, the second method used the determined<br />statistics as features of the original signal ,then multiplied it with constant<br />wavelet filter based on Gaussian ,while the third method is similar to first<br />method, except it depend on entire signal instead of each pool size. The<br />proposed methods are combined with other standard methods such as max<br />and pooling. The experiments are performed on different datasets , and the<br />results show that the proposed methods perform or outperform other<br />methods and can increase performance of the (CNN).</p>

Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison

Short-term traffic flow prediction plays a key role of Intelligent Transportation System (ITS), which supports traffic planning, traffic management and control, roadway safety evaluation, energy consumption estimation, etc. The widely deployed traffic sensors provide us numerous and continuous traffic flow data, which may contain outlier samples due to expected sensor failures. The primary objective of the study was to evaluate the use of various smoothing models for cleaning anomaly in traffic flow data, which were further processed to predict short term traffic flow evolution with artificial neural network. The wavelet filter, moving average model, and Butterworth filter were carefully tested to smooth the collected loop detector data. Then, the artificial neural network was introduced to predict traffic flow at different time spans, which were quantitatively analyzed with commonly-used evaluation metrics. The findings of the study provide us efficient and accurate denoising approaches for short term traffic flow prediction.

Vein palm recognition model using fusion of features

One of the most promising mechanisms in the field of security and information safety is authentication based on palm vein. The main reasons that vein palm becomes an authentication method is because of its distinctive privacy, as it is difficult to manipulate or change its results, because of the location of the vein within the palm. With the use of this technology, it has become easy to maintain data from unauthorized access and unwanted persons. In this work proposed model are suggested that contain four stages to reach the results: in the first stage is the pre-processing stage where histogram equation was used to enhance the image and the properties are shown, the second stage is the extracting the properties where, Gabor filter and 2-discrete wavelet filters are suggested for features extraction, where it is considered one of the most important filters used to extract the features, as well as in the third stage "PCA" are used for data or features reduction, because of its advantages in analyzing the features and reducing the spacing between them. As for the last stage, the Euclidean distance used to measure the spacing. The results were acceptable and convincing, since the similarity ratio 96.2%. These results were obtained after several tests and using the Gabor filter with 2D-discrete wavelet transform and principal component analysis (PCA), I got the best results.

Some wavelet filters to estimate non-parametric GAM models with application and simulation

This study shed light on the method of estimating the GAM based on Smoothing splines repetitive graders . The Wavelet Shrinkage method was used as a paving of data when estimating the GAM by using some wavelets as filters in calculating the wavy intermittent transformation, including (Haar Wavelet, Daubecheis Wavelet, Coiflets Wavelet, wavelet Least Asymmetric) with one of the types of threshold cutting which is Soft Threshold Thresholding to obtain modified coefficients for intermittent wavelet transformation with explanatory and response variables and dependence on estimating the WGAM model with a comparison of the results of all methods with some comparative statistical criteria, And that is by employing the simulation method as well as through real data analysis, and for this purpose data was collected from Ibn Sina Teaching Hospital (Al-Wafa Specialist Center for Diabetes and Endocrinology Consultant Short Stature) for Nineveh Governorate - 2019, for cases of short stature, and a program was used R for the purpose of writing some code for the purpose of obtaining the desired results from this research.

Low‐complexity block tree image coder for visual sensor networks

The wavelet block tree coding (WBTC) algorithm is an efficient wavelet-based image coder. In this coder, multiple spatial orientation trees are combined together to make a single block tree. It uses three ordered lists to keep track of significant/insignificant coefficients and sets while coding, which increases its memory requirement as well as computational complexity. Also, it uses memory inefficient conventional discrete wavelet transform (DWT) to compute the transformed coefficients. In this study, a Low-Complexity Block Tree Coding (LCBTC) algorithm that uses two state-tables and two very small lists, is proposed. Similar to WBTC, it also uses sorting and refinement passes in each bit-plane. However, it encodes the coefficients in block-tree manner using depth-first search approach to reduce the computational complexity. It uses DWT coefficients obtained from modified fractional wavelet filter (MFrWF) rather than conventional DWT, which further reduces the overall memory and complexity of the image coder. The simulation results show that the memory requirement and computational complexity of LCBTC is much less than WBTC and other state-of-the-art coding algorithms. These features make the image coder a better candidate for compression in memory constrained and real-time visual sensor networks.

Some wavelet filters to estimate non-parametric GAM models with application and simulation.

Some wavelet filters to estimate non-parametric GAM models with application and simulation.

Constraining neutrino masses with weak-lensing multiscale peak counts

Massive neutrinos influence the background evolution of the Universe as well as the growth of structure. Being able to model this effect and constrain the sum of their masses is one of the key challenges in modern cosmology. Weak-lensing cosmological constraints will also soon reach higher levels of precision with next-generation surveys like LSST, WFIRST and Euclid. We use the MassiveNus simulations to derive constraints on the sum of neutrino masses $M_{\nu}$, the present-day total matter density $\Omega_{\rm m}$, and the primordial power spectrum normalization $A_{\rm s}$ in a tomographic setting. We measure the lensing power spectrum as second-order statistics along with peak counts as higher-order statistics on lensing convergence maps generated from the simulations. We investigate the impact of multiscale filtering approaches on cosmological parameters by employing a starlet (wavelet) filter and a concatenation of Gaussian filters. In both cases peak counts perform better than the power spectrum on the set of parameters [$M_{\nu}$, $\Omega_{\rm m}$, $A_{\rm s}$] respectively by 63$\%$, 40$\%$ and 72$\%$ when using a starlet filter and by 70$\%$, 40$\%$ and 77$\%$ when using a multiscale Gaussian. More importantly, we show that when using a multiscale approach, joining power spectrum and peaks does not add any relevant information over considering just the peaks alone. While both multiscale filters behave similarly, we find that with the starlet filter the majority of the information in the data covariance matrix is encoded in the diagonal elements; this can be an advantage when inverting the matrix, speeding up the numerical implementation.

Comparative Performance Analysis of Music Audio Denoising Using Wavelet Technique

Mostly communication channels are experiencing noises and it is very difficult to recover from the original signals without any noise. Denoising can be performed with various filtering techniques that can be separate the noise signal from the original signal. Filtering techniques have been proposed for noise removal from the digital musical signals. The overall performance of the filtering technique is less effective. Denoising of an audio signal is an area under development. In this article, music signals are considered input signals. Denoising techniques are compared based on different filter design techniques. A technique of filtering of noise is implemented and compared using Signal-to-Noise Ratio (SNR). Here for comparison, the Butterworth filter and different types of wavelet filters are used for denoising the musical signals. Daubechies wavelet techniques work better than existing methods is proven by SNR.

Remote-Sensing Image Scene Classification With Deep Neural Networks in JPEG 2000 Compressed Domain

To reduce the storage requirements, remote sensing (RS) images are usually stored in compressed format. Existing scene classification approaches using deep neural networks (DNNs) require to fully decompress the images, which is a computationally demanding task in operational applications. To address this issue, in this paper we propose a novel approach to achieve scene classification in JPEG 2000 compressed RS images. The proposed approach consists of two main steps: i) approximation of the finer resolution sub-bands of reversible biorthogonal wavelet filters used in JPEG 2000; and ii) characterization of the high-level semantic content of approximated wavelet sub-bands and scene classification based on the learnt descriptors. This is achieved by taking codestreams associated with the coarsest resolution wavelet sub-band as input to approximate finer resolution sub-bands using a number of transposed convolutional layers. Then, a series of convolutional layers models the high-level semantic content of the approximated wavelet sub-band. Thus, the proposed approach models the multiresolution paradigm given in the JPEG 2000 compression algorithm in an end-to-end trainable unified neural network. In the classification stage, the proposed approach takes only the coarsest resolution wavelet sub-bands as input, thereby reducing the time required to apply decoding. Experimental results performed on two benchmark aerial image archives demonstrate that the proposed approach significantly reduces the computational time with similar classification accuracies when compared to traditional RS scene classification approaches (which requires full image decompression).

Discrete Wavelet Design for Bearing Fault Diagnosis Using Particle Swarm Optimization

Rolling bearings are widely used in a large variety of industrial applications. Therefore, it is necessary to provide an efficient fault detection and diagnosis mechanism to prevent component failure and poor performance during operation. This paper proposes a novel classification scheme based on the design of discrete wavelets best adapted to vibration signal analysis in order to identify and properly classify rolling bearing defects. Through polyphase representation of the wavelet filter bank, and using the particle swarm optimization (PSO) algorithm, the appropriate discrete wavelet associated filters are optimized to achieve the best fault classification accuracy. Simulation results show that the proposed wavelet design approach outperforms the well-known standard wavelets regardless the employed classifier and leads to an average fault classification improvement of about 2%.

Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm

Meyer wavelet filters are the key building blocks of empirical wavelet transform. In mechanical fault diagnosis, however, the boundaries of Meyer wavelet filters are usually defined empirically. In order to solve the problems, this paper proposes a new index called harmonic infinite-taxicab norm to guide grasshopper optimization algorithm to primarily optimize a band-pass filter and thus, concurrently and secondarily optimize a low-pass filter and a high-pass filter of Meyer wavelet. The proposed index is inspired by spectral Lp/Lq norm and it is closely related to fault characteristic frequency of rotating machinery. In addition, only three Meyer wavelet filters are demanded in each iteration of optimization. The effectiveness of the proposed method is validated by comparing with fast kurtogram method on analyzing faulty bearing data and gearbox data.