Multi-level Wavelet Decomposition Research Articles

Automated Detection System for Diabetic Retinopathy Using Two Field Fundus Photography

Diabetic retinopathy (DR) is a leading cause of vision loss, caused by damage to the retina from complications of diabetes. Analysis of the retinal photographs for key characteristics of DR can result in early diagnosis and better management of DR. This paper presents a method for automated analysis and classification of the retina as DR or non-DR using two-field mydriatic fundus photography. The optic disc region is located by multi-level wavelet decomposition and recursive region growing from an automatically identified seed point. Blood vessels are extracted by applying histogram analysis on the two median filtered images. Red lesions are detected using three stage intensity transformation and white lesions from multi-level histogram analysis. The final classification of the retina as DR or non-DR is based on an aggregate of the lesions extracted from each image. The proposed method has been validated against diagnosis by a panel of expert ophthalmologists on images from 368 patients. The observed sensitivity and specificity were 80% and 50% respectively. The results show that automated screening based on two-field photography can be applied in routine screening.

Wavelet based Multilevel Sub-Band Adaptive Thresholding for Image Denoising using Modified PSO Algorithm

The image de-noising is one in all the foremost studied areas within the field of image processing. There are many ways (like communication channel, imperfect sensors, interference etc.) by which the noise may affect the image. Depending upon the nature of noise and the image many techniques has been already proposed. However for any technique it is difficult to operate on different level of noises over different kind of images (like SAR images, X- ray images, Ultrasound images etc.). The best possible solution for such cases is to use adaptive techniques. In this paper we are presenting a multilevel wavelet decomposition based adaptive thresholding technique which utilizes the modified Particle Swarm Optimization (PSO) algorithm to find out the optimal values for thresholds and level of decompositions for given objective function. The modification of PSO is done through random perturbation in particle velocities which induces small randomness in new particle position estimation. This randomness can effectively increase the particle search space, which ultimately provides a much better solution than the conventional PSO. Finally the proposed algorithm is validated by testing it over different kind of images corrupted by different values of noise.

An Accurate Approach for Automatic Segmentation of Power Distribution Voltage Waveforms

This paper addresses one of the fundamental steps in automatic waveform analysis: transient segmentation. We present a new approach which incorporates the advantages of a multilevel wavelet decomposition and the representation of the support vector data description. Real data from a monitoring system developed for lightning overvoltage detection in overhead distribution power lines was used for comparison and validation of segmentation performance. The experiments involve the proposed segmentation approach and usual segmentation methods, such as Kalman filtering, autoregressive models, and standard discrete wavelet transform. The results show that the proposed segmentation method based on DWT+SVDD yields better overall accuracy for transient segmentation when compared to currently used methods, demonstrating the potential for applications in oscillographic recorders for smart distribution networks, where identification, characterization, and mitigation of events are critical for network operation and maintenance.

Near-Threshold Energy- and Area-Efficient Reconfigurable DWPT/DWT Processor for Healthcare-Monitoring Applications

This brief presents an energy- and area-efficient discrete wavelet packet transform (DWPT) processor design for power-constrained and cost-sensitive healthcare-monitoring applications. This DWPT processor employs recursive memory-shared architecture to achieve low hardware complexity while performing required arbitrary-basis DWPT decomposition. By exploiting inherent characteristics of different physiological signals through an entropy statistic engine, the DWPT processor core can be reconfigured to compute multilevel wavelet decomposition with effective time and frequency resolution. Various design techniques from algorithm to circuit levels, including reconfigurable computing, lifting scheme, dual-port pipeline processing, near-threshold operation, and clock gating, are applied to achieve energy efficiency. With a 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="TeX">$\mu\hbox{m}$</tex-math></inline-formula> CMOS technology at 0.5 V and 1 MHz, the DWPT core only consumes 26 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="TeX">$\mu\hbox{W}$</tex-math></inline-formula> for performing three-level 256-point DWPT decomposition with entropy statistic calculation. When integrated in an ARM Cortex-M0-based biomedical system-on-a-chip test platform, the DWPT processor achieves processing acceleration by three orders of magnitude and reduces energy consumption by four orders of magnitude compared with CPU-only implementations.

Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches

In remote sensing, satellite images acquired from sensors provide either high spectral or high spatial resolution. The pansharpening framework is applied to remote-sensing systems to enhance the spatial quality of coarse-resolution multispectral (MS) images using information from panchromatic imagery. A multidecomposition pansharpening approach combining MS and panchromatic (PAN) images is proposed in this paper in order to bring the resolution of the low-resolution MS imagery up to that of the panchromatic images. In particular, multilevel wavelet decomposition is applied to the luminance-chrominance (YUV) space transformation (taking into account the red green and blue (RGB) bands) or extended-YUV transformation (taking into account the near infrared (NIR) band in addition to RGB) of the original MS channels, where geometrical details from the panchromatic image are introduced into the MS ones. Our approach contains a preprocessing step that consists of homogenizing the luminance, Y, and the panchromatic image reflectance, which are, respectively, a value integrated over a wavelength spectrum and simply a linear combination of some values in the same spectrum. Hence, as the panchromatic image reflectance and luminance reflectance correspond to different measurements, they do not correspond to the same physical information, which results in a difference between their histograms. Therefore, simple histogram matching is traditionally applied to panchromatic data to fit it to the luminance to avoid colour distortion after fusion. However, as the transformation concerns just the details of the panchromatic and MS images, a new scheme for matching the images which ignores the divergence between their approximations and maximizes the resemblance between their details is proposed in this work. After that, the fusion approach is applied, and in contrast to the original approach where the details of the fused MS luminance are set equal to the PAN luminance, we propose an adaptive approach in which just a part of the PAN details proportional to the similarity between the luminance and lowered PAN image is taken. Indeed, high-resolution geometrical details cannot be similar if the low-resolution details are not in good agreement. Besides, as the agreement between PAN and MS images depends on the occupation class, we have created a segmentation map and then computed separately the correlation in each region. Finally, the evaluation is done based on QuickBird and Pleiades-1A data sets showing rural and suburban areas. When compared to recent methods, our approach provides better results.

Obtaining Spectrum Matching Time Series Using a Reweighted Volterra Series Algorithm (RVSA)

In this paper, we introduce a novel algorithm for morphing any accelerogram into a spectrum matching one. First, the seed time series is re-expressed as a discrete Volterra series. The first-order Volterra kernel is estimated by a multilevel wavelet decomposition using the stationary wavelet transform. Second, the higher-order Volterra kernels are estimated using a complete multinomial mixing of the first-order kernel functions. Finally, the weighting of every term in this Volterra series is optimally adapted using a Levenberg–Marquardt algorithm such that the modified time series matches any target response spectrum. Comparisons are made using the SeismoMatch algorithm, and this reweighted Volterra series algorithm is demonstrated to be considerably more robust,matching the target spectrum more faithfully. This is achieved while qualitatively maintaining the original signal’s non-stationary statistics, such as general envelope, time location of large pulses, and variation of frequency content with time.

Study on Classification of Wheeled and Tracked Vehicles Based on Micro-Doppler Effect and Multilevel Wavelet Decomposition

Study on Classification of Wheeled and Tracked Vehicles Based on Micro-Doppler Effect and Multilevel Wavelet Decomposition

Algorithm of Digital Watermark Based on Wavelet

Analyze a human visual model based wavelet domain digital watermark algorithm and improved it. First conduct multi-level wavelet decomposition on original image and modify medium frequency coefficient, and then adapt the algorithm to embed the watermark into the source image. The experiment shows that the watermark embedded image enjoys better imperceptibility and robustness to JPEG compression, add gaussian noise, contrast enhancement e.t. traditional image processing operations.

A Novel Full-Reference Color Image Quality Assessment Based on Energy Computation in the Wavelet Domain

AbstractThis article presents a novel full-reference (FR) image quality assessment (QA) algorithm by depicting the sub-band characteristics in the wavelet domain. The proposed image quality assessment method is based on energy estimation in the wavelet-transformed image. Image QA is achieved by applying a multilevel wavelet decomposition on both the original and the enhanced image. Next, the wavelet energy (WE) and vector are computed to obtain the percentage of the energy that corresponds to the approximation and the details, respectively. Further, the approximate and detailed energy levels of both the original and the enhanced images are compared to formulate an image quality assessment. Numerous experiments are conducted on a dozen of image enhancement algorithms. The results presented show that the image with poor contrast in the foreground than the background has continuous regular coefficient values. The probability density function for such an image has a relatively lower WE and skewness compared with the background. The proposed scheme not only evaluates the global information of an image but also estimates the fine, detailed changes in an enhanced image. Thus, the proposed metric serves as an objective and effective FR criterion for color image QA. The experimental results presented confirm that the proposed WE metric is an efficient and useful metric for evaluating the quality of the color image enhancement.

An ECT System Based on Improved RBF Network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow

Electrical capacitance tomography (ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance. To solve the nonlinear and ill-posed inverse problem: image reconstruction of ECT system, this paper proposed a new image reconstruction method based on improved radial basis function (RBF) neural network combined with adaptive wavelet image enhancement. Firstly, an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured. Then, for better image quality, adaptive wavelet image enhancement technique was emphatically analyzed and studied, which belongs to a space-frequency analysis method and is suitable for image feature-enhanced. Through multi-level wavelet decomposition, edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band; noise distribution in the space-frequency domain can be estimated based on statistical characteristics; after that a self-adaptive edge enhancement gain can be constructed. Finally, the image is reconstructed with adjusting wavelet coefficients. In this paper, a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm. Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement, and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal (PC)/air].

Scale-up of an electrical capacitance tomography sensor for imaging pharmaceutical fluidized beds and validation by computational fluid dynamics

The aim of this research is to apply electrical capacitance tomography (ECT) in pharmaceutical fluidized beds and scale up the application of ECT from a lab-scale fluidized bed to a production-scale fluidized bed. The objective is to optimize the design of the production-scale fluidized bed and to improve the operation efficiency of the fluidization processes. This is the first time that ECT has been scaled up to a production-scale fluidized bed of 1.0 m diameter and batch process capacity of 100 kg in a real industrial environment. With a large-scale fluidized bed in a real industrial environment, some key issues on the ECT sensor design must be addressed. To validate ECT measurement results, a two-phase flow model has been used to simulate the process in a lab-scale and pilot-scale fluidized bed. The key process parameters include solid concentration, average concentration profiles, the frequency spectrum of signal fluctuation obtained by the fast Fourier transfer (FFT) and multi-level wavelet decomposition in the time domain. The results show different hydrodynamic behaviour of fluidized beds of different scales. The time-averaged parameters from ECT and computational fluid dynamics are compared. Future work on the ECT sensor design for large-scale fluidized beds are given in the end of the paper.

Visualization of three-dimensional video sequence images in real-time mode on the basis of multilevel wavelet decomposition

(DM) on the basis of stereo pairs formed by adjacent frames with the use of multilevel WAF wavelet decom� position (MWAF); the depthmap correction by means of the dynamic compression; the synthesis of the anaglyph by the interpolation using the method of the closest adjacent pixel (NNI); and, finally, the visu� alization of the 3Dimage video sequence. The block diagram for the algorithm proposed is presented in Fig. 1. The multilevel decomposition of the wavelet func� tion is formed on the basis of the discrete wavelet transform (DWT) with respect to three components (6); namely, horizontal, vertical, and diagonal compo� nents can be represented as

Low-complexity wavelet-based scalable image &amp; video coding for home-use surveillance

We study scalable image and video coding for the surveillance of rooms and personal environments based on inexpensive cameras and portable devices. The scalability is achieved through a multi-level 2D dyadic wavelet decomposition featuring an accurate low-cost integer wavelet implementation with lifting. As our primary contribution, we present a modification to the SPECK wavelet coefficient encoding algorithm to significantly improve the efficiency of an embedded system implementation. The modification consists of storing the significance of all quadtree nodes in a buffer, where each node comprises several coefficients. This buffer is then used to efficiently construct the code with minimal and direct memory access. Our approach allows efficient parallel implementation on multi-core computer systems and gives a substantial reduction of memory access and thus power consumption. We report experimental results, showing an approximate gain factor of 1,000 in execution time compared to a straightforward SPECK implementation, when combined with code optimization on a common digital signal processor. This translates to 75 full color 4CIF 4:2:0 encoding cycles per second, clearly demonstrating the realtime capabilities of the proposed modification.

Technologies for Pattern Recognition of Late Potentials atria: classification approaches

In this paper the problem of atrial late potentials (ALP) recognition is solved by the formation of their signs in the basis of eigenvectors. Covariance matrix is formed for an ensemble of wavelet detail coefficients obtained by multi-level wavelet decomposition of electrocardiosignals (ECS). The results of cluster analysis in a model experiment on the classification of ECS with and without ALP on the background noise are presented.

Energy and fractal characteristics of physiological and pathological tremors of the human hand

Tremors (involuntary shaking) were compared in healthy subjects and patients with the tremulous form of Parkinsonism sustaining isometric effort of the hand. For this purpose, fractal analysis and multilevel wavelet decomposition of tremor were used, and the energy characteristics determined on the basis of the spectral density of the energy of detailed components obtained at different levels of decomposition. The calculated fractal and energy characteristics of the tremor of healthy subjects were significantly lower than those of patients. If the patients took antiparkinsonian drugs at their usual doses, the characteristics shifted towards normal values. The decrease in the fractal dimension indicates an increase in the strength of the correlation in the dynamics of involuntary shaking. Thus, the characteristics studied allow not only physiological and pathological tremors to be discriminated, but also the strategy of selection of optimal drugs for relieving Parkinsonian tremor to be developed.

OPTIMAL RATE ALLOCATION FOR LOGO WATERMARKING

Compression of logo as an image is addressed in logo watermarking, in which a framework of joint Human Vision System (HVS) model and rate allocation theory in the wavelet domain is applied. Under this framework, a novel logo watermarking algorithm using reversible discrete wavelet transform is proposed. Based on multi-level wavelet decomposition of both host and logo images, a well-known HVS model is applied to locate the visually insensitive area in the host for embedding, while the rate allocation theory determines how the logo is compressed and embedded by using the statistical characteristics of the logo. For a given overall embedding distortion, quantization step-sizes of different logo subbands are analytically determined to maximize the fidelity of the extracted logo under the imperceptibility constraint. The adaptive system is thus applicable to different hosts and logos without tuning the parameters manually. It is proved to be robust against various types of attacks and is quite suitable for hardware implementations. Since logo is considered as an image in this new algorithm, the watermarking approach developed can be extended in general to image-in-image embedding.

Two Dimensional Wavelet Coefficient Statistics for Sea Bottom Classification

In this paper we examine the classification of different seafloors based on the analysis of images obtained by side-scan sonar. For this purpose, we apply various two dimensional multilevel wavelet decomposition schemes on images obtained from three different seafloor types, i.e., sand ripples, rocks and sands, and then we examine the statistics of the corresponding wavelet coefficients. The observed Probability Density Functions (pdf) are modeled using various theoretical distributions such as the Alpha Stable, Sum of Gaussians, Log-normal. The parameters of the fit are subsequently used to classify the side scan sonar images according to well known cluster analysis techniques. The use of the energy of the wavelet coefficients as a tool for side scan sonar image classification is also evaluated. A new unsupervised classification scheme based on the pdf fitting parameters is proposed.

Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal

In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.

Time division multiplexing based method for compressing ECG signals: application for normal and abnormal cases

The proposed ECG compression method combines three major approaches based on time division multiplexing (TDM) and multilevel wavelet decomposition followed by parametrical modelling. Before applying these techniques, a pre-processing step is required, which consists of detecting and aligning different beats. Even though this compression method is regarded as a lossy method, we will show how a high compression ratio (CR) can be achieved by preserving the major medical information within the ECG. Several normal and abnormal signals from various databases are used to evaluate the performance of the proposed technique.

Texture analysis with variational hidden Markov trees

A variational Bayes formulation of the hidden Markov tree (HMT) model is proposed for texture analysis, utilizing a multilevel wavelet decomposition of imagery. The variational method yields an approximation to the full posterior of the HMT parameters. Texture classification is based on the posterior predictive distribution or marginalized evidence, with example results presented.