Double-density Discrete Wavelet Research Articles

A Retina-inspired Sampling Method for Coding and Compression for Enhancement Image Based on Discrete Double Density Wavelet Transformer

The retina, a layer of light-sensitive tissue on the inner surface of the eye, is what allows for vision. That propose to design and analytically examine an image quantizer coding that is inspired by the way the retina manages and compresses visual features based on past studies. Retina-inspired coding is a new cipher algorithm which is very promising. Comparing the Double-Density Discrete Wavelet Transform (DDDWT) to earlier traditional approaches, it is more difficult but also produces excellent results. This efficient coding is credited to a streamlined account that, by the way, deals with 2-D and 3-D pictures, transformation matrices, and the conversion of the number DDWT as if via matrix multiplication. Naturally, this code makes a lot of interesting points of view available. In this proposal, we'll try to show how various theoretical model extensions can be applied to applications in video surveillance, information technology, and neuroscience. Therefore, that believe that using a multidisciplinary approach will help system achieve objectives. This strategy would apply signal processing methods with data from neurophysiology. There are hoping that our efforts will result in some innovative new coding algorithms. The proposed codes will be implemented in MATLAB for ease of experimentation. To implement this code more successfully, a low-level programming language is required. Index Terms— DDDWT, Retina-inspired, Coding.

FPGA design of FFT based intelligent accelerator with optimized Wallace tree multiplier for image super resolution and quality enhancement

The super-resolution (SR) method has been widely used to improve the resolution and quality of natural images, and it is currently being employed in medical imaging. Recently, many techniques have been introduced by researchers to improve the resolution and quality of the image. However, these approaches generate blurring and jagged edges in images. Hence, this article introduces novel deep learning (DL) with a fast Fourier transform (FFT) based intelligent approach (IA) to improve the quality and resolution of the image. A convolutional neural network (CNN) is emphasized for quality enhancement and resolution to recover lost information during image acquisition. This work undergoes three significant stages: de-noising, segmentation, and resolution improvement of the noisy input image. The double density discrete wavelet transform (DDDWT) technique is introduced to remove the noise and improve the visual image’s quality. Finally, the image resolution and quality enhancement are done using CNN with overlap and add (OA)-FFT based Wallace tree multiplier-red deer optimization (WT-RDO) technique. In the experimental scenario, the quality enhancement and image resolution outcome achieved accuracy of 97 % and 98 %, precision of 97 % and 97 %, sensitivity of 96 % and 98 %, specificity of 96 % and 98 %, PSNR of 30 dB and 26 dB, structural similarity index (SSIM) of 0.86 and 0.85, mean square error (MSE) of 0.01, mean absolute error (MAE) of 0.8, the time complexity of 61, power of 0.08 W, delay of 0.6 ns and throughput of 3.2 MB/s.

AI-empowered malware detection system for industrial internet of things

With the significant growth in Industrial Internet of Things (IIoT) technologies, various IIoT-based applications have emerged in the last decade. In recent years, various malware-based cyber-attacks have been reported on IIoT-based systems. Thus, this research work outlines the design of an efficient Artificial Intelligence (AI)-empowered zero-day malware detection system for IIoT. In this paper, a hybrid deep learning-based malware detection framework is proposed in which a Double-Density Discrete Wavelet Transform (D3WT) is used for feature extraction and a hybrid of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is used for identification and classification of malware. The assessment of the proposed framework is evaluated using three datasets such as IoT malware, Microsoft BIG-2015 and Malimg dataset. Experimental results show that the proposed model achieved 99.98% accuracy on the IoT malware, 96.97% accuracy on the Microsoft BIG-2015 and 99.96% accuracy with the Malimg dataset.

Adaptive Hybrid Bivariate Double Density Discrete and Complex Wavelet for Image Denoising

Image denoising is an important step in eliminating any noise impact in any image transmission process. Recently we presented two approaches for Bivariate based image denoising. They were Double Density Discrete Wavelet Transform (DD DWT) and Double Density Dual Tree Complex Wavelet Transform (DD CWT). In both techniques we decomposed noisy images with either DD DWT or DD CWT decompositions and then applied the Bivariate based denoising technique for noise removal. In this paper we propose an adaptive hybrid technique for Bivariate based image denoising that is based on the synthesis of DD-DWT bands or DD-CWT bands but with different weights, to deliver enhanced image features with less denoising impact especially around image edges, which is the most effected by noisy transmission channels. This proposed technique has been also enhanced by edge sharpening and Eigen analysis, as two separate stages. Simulation result comparisons have been performed between the proposed hybrid band adaptive DD-DWT and DD-CWT technique and the two primary techniques DD-DWT, DD- CWT, as well as other superior literature techniques such the original bivariate denoising technique with both original Complex Wavelet Transform and Double Density decompositions. This work in specific compares between Double Density DWT and Double Density CWT decompositions, proposes new filter design that suits each of them and proposes a hybrid technique between as will be shown.

Hybrid PSO-RBFNN and Proposed Algorithms of DDDWT for the Heart Disease Classification

This paper introduced a Particle Swarm Optimization-Radial Basis Function Neural Networks (PSO-RBFNN)-based system for heart disease detection that used the PSO algorithm to optimize RBFNN parameters. The newly developed signal digital algorithm presents the results of a new image contrast enhancement approach using Double Density Discrete Wavelet transform DDDWT for extraction of features, using adaptive DDDWT for the elimination of noise, and the use of PSO and ANN methods to classify the output from the Electrocardiogram (EGGS). It also provides identification of all techniques and MATLAB codes used to improve the processes. This approach merged the global search power of the PSO algorithm with the high efficiency of RBFNN's local optimums, overcome the inconsistency of the PSO algorithm and the RBFNN downside, quickly leading to a local minimum. The results show that, as compared to other approaches, the PSO-RBFNN model of heart disease diagnosis is highly accurate in detecting and predicting.

Integrating compositional pattern-producing networks and optimized convolution neural networks using deep learning techniques for detecting brain abnormalities

In brain abnormality approach, accurate and reliable diagnosis is a critical component, because it provides potential abnormality in tissues as well as functional structures significantly to demarcate surgical plan. In the recent past, various diagnosing procedures have been carried out such as Double Density Discrete Wavelet Transform (DDDWT), Support Vector Machine (SVM) classifier, Convolutional Neural Networks etc., whereas treatment outcome and planning are the critical components. Build upon the successful deep learning approach, a novel brain abnormality diagnostic method has been developed by integrating Compositional Pattern Producing (CPP) and Optimized Convolutional Neural Networks (OCNN) in an unified framework. This hybrid approach includes Independent Component Analysis (ICA) with parallel factor and region of interest (ROI) for preprocessing, training CPP using image patches and fine tuning CPP-OCNN using image slices for segmentation as well as for extraction. This model could segment the images by slices than patches which are more accurate and less complex in segmentation with optimized kernel SVM classifier for abnormality detection. The system is evaluated based on MRI imaging datasets provided by CHB-MIT scalp EEG of micro bleeds dataset and efficiently validated with the help of the experimental results by minimizing the Root mean square and by improving the accuracy, smoothness, correlation, sensitivity and specificity using state of the art techniques.

A Compression Original Image Based On The DDWT Technique And Enhancement SNR

Generally, Discrete wavelet transform (DWT) are good perform a when little to no simple mathematical operations in the wavelet basis, in many applications, wavelet transforms can be severely truncated compressed and retain useful information Image compression. Though, DWT and the divided wavelet transform, still suffering from Poor directionality Lack of phase information, and Shift- sensitivity, which is a major drawback in most the communications systems. The Double-Density Discrete Wavelet Transform (DDDWT) achieves great results compared to previous conventional methods less complexity. Credited with this good result, so due to a simplified account that deal with two-dimensional and three-dimensional images by the way and transformation matrices as if through a matrix multiplication between the picture and the conversion of number DDWT. Moreover, the form of repeated goal is achieved with the optimization process for the appropriate application.

Signal Denoising Using Double Density Discrete Wavelet Transform

Reality signals do not exist without noise. Wavelet transform based denoising seem to be a powerful tool for suppressing noise in signals. In this paper, we investigate the using of double density discrete wavelet transform “DD-DWT” which based on one scaling function and two wavelet functions, for signal denoising and comparing its performance with the traditional DWT. Three groups of additive White Gaussian Noise levels (5 dB, 3 dB, 2 dB) are added to some standard test signals with both hard and soft threshold function to evaluate the performance of each method in term of Root Mean Square Error (RMSE) and Signal to Noise Ratio (SNR). Experiment results show that DD-DWT performs better than traditional DWT in both RMSE and SNR especially at low SNR.

An Efficient CRT based Digital Image Watermarking using Double Density Wavelet Transform

Digital Watermarking is an unpretentious and effective approach to afford copyright fortification. Watermark transparency is obligatory primarily for copyright protection. The challenge is to introduce a digital watermark that is both transparent and highly robust to common signal processing and possible attacks. The two basic requirements for an effective watermarking scheme, robustness and transparency, conflict with each other.This paper proposesa watermarking technique for digital gray scale images that is based on utilizing congruence’s in number theory and its generalizations in abstract algebra which have been developed in the context of image compression. Watermarking scheme is based on a Chinese Remainder Theorem along with Double Density Discrete Wavelet Transform framework individually which allows for the possibility of directly watermarking the Image bitstream, Watermark region selection is based on transformation level. In proposed scheme, initially, the cover image and watermark are transformed into spatial domain using Double Density Wavelet Transform and then singular values of these transformed images are combined using CRT coefficients. Our scheme is shown to provide very good results both in terms of image transparency and robustness. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. Experimental results are provided in terms of Peak signal to noise ratio (PSNR), Mean Squared Error (MSE), Correlation and Weighted Peak signal to noise ratio (WPSNR) to demonstrate the effectiveness of the proposed algorithm.

Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM

Aiming at the problems of low accuracy, poor universality and functional singleness for seizure detection, an effective approach using wavelet-based non-linear analysis and genetic algorithm optimized support vector machine (GA-SVM) is proposed to deal with five challenging classification problems in this study. Instead of the traditional discrete wavelet transform (DWT), we attempt to explore the ability of double-density discrete wavelet transform (DD-DWT) to decompose the original EEG into specific sub-bands. The Hurst exponent (HE) and fuzzy entropy (FuzzyEn) are extracted as input features and then fed into two classifiers. On using these ranking non-linear features, the GA-SVM configured with fewer features is found to achieve the prominent classification performance for various combinations such as AB-CD-E, A-D-E, ABCD-E, C-E and D-E, achieving accuracies of 99.36%, 99.60%, 99.40%, 100% and 100%, respectively. The results have indicated that our scheme is not only appropriate in solving problems with multiple classes but also of lower complexity and better expansibility. These characteristics would make this method become an attractive alternative for actual clinical diagnosis.

New features for speech enhancement using bivariate shrinkage based on redundant wavelet filter-banks

In most of the wavelet based speech enhancement methods, it is assumed that the wavelet coefficients are independent of each other. However, investigating the joint histogram of the wavelet coefficients reveals some dependencies among them. In this regard, Sendur proposed a probability density function (pdf) that models the relation between a wavelet coefficient of image signal and its parent. Then, this pdf is utilized to propose a bivariate shrinkage function which uses the dependencies between the child–parent wavelet coefficients of Image signals to enhance the noisy images. In this paper, we intend to find wavelet structures which are more suitable for speech enhancement based on bivariate shrinkage. We show that the dependencies between the child–parent wavelet coefficients can only be modeled rather easily up to two stages of two-channel discrete wavelet transform using the Sendur's pdf. However, the bivariate shrinkage function works better in three-channel redundant wavelet filter-bank with dilation 2, since it has a joint distribution which is similar to the Sendur's pdf up to the fourth stage of decomposition for speech signals. Furthermore, we show that three-channel higher density wavelet obtained by eliminating the downsampling part of the third channel is more suitable for the bivariate shrinkage function when it is utilized for speech enhancement. Then, appropriate filter values for three-channel higher density wavelet filter-bank are found. Moreover, we propose four-channel double density discrete wavelet filter-bank which leads to some improvement in speech enhancement results. Since the probability of speech presence is higher in lower frequencies, we suggest level-dependent bivariate shrinkage. Finally, Sendur bivariate shrinkage is optimized for speech enhancement and new methods are proposed by combining former successful methods with the bivariate shrinkage function.

Speech frame recognition based on less shift sensitive wavelet filter banks

The wavelet transform possesses multi-resolution property and high localization performance; hence, it can be optimized for speech recognition. In our previous work, we show that redundant wavelet filter bank parameters work better in speech recognition task, because they are much less shift sensitive than those of critically sampled discrete wavelet transform (DWT). In this paper, three types of wavelet representations are introduced, including features based on dual-tree complex wavelet transform (DT-CWT), perceptual dual-tree complex wavelet transform, and four-channel double-density discrete wavelet transform (FCDDDWT). Then, appropriate filter values for DT-CWT and FCDDDWT are proposed. The performances of the proposed wavelet representations are compared in a phoneme recognition task using special form of the time-delay neural networks. Performance evaluations confirm that dual-tree complex wavelet filter banks outperform conventional DWT in speech recognition systems. The proposed perceptual dual-tree complex wavelet filter bank results in up to approximately 9.82 % recognition rate increase, compared to the critically sampled two-channel wavelet filter bank.

English

In this paper a novel turbulence mitigation algorithm is proposed based on Double density discrete wavelet transform based fusion technique. In this scheme DD-DWT is applied to ROI part of selected frame of video sequence to decompose image into sub-bands and then fusion technique is applied to each ROI frame so that obtained output video is distortion free.We compare our method with state-of-art criteria like DTCWT based fusion technique; we prove that our technique yields better results.

Ground roll attenuation using real and complex DWT based methods

This paper deals with ground roll attenuation using double density and dual-tree complex discrete wavelet transforms. These transforms use a new denoising method instead of well-known hard and soft thresholdings and Wavelet Domain Ground roll Analysis (WDGA) applied in finding and attenuating the ground roll energy in the wavelet domain. The dual-tree complex wavelet transform (DTCWT), and double density discrete wavelet transform (DDDWT) have two wavelets and one scaling function and are nearly shift-invariant. Also they have redundancy factors of 2, and their wavelets are smoother than other orthogonal wavelets. Based on this fact, they can be considered as replacement methods for such aims. Recognizing the performance of each method is required to show which method is more suitable and efficient. In the present study, distinct wavelets and scaling function of each method have been applied to the data, and also the data are denoised by Wavelet Domain Ground roll Analysis technique. Additionally, for the inverse transform, the coefficients of the noise are removed. And finally for concluding, the results of all methods are compared with the f-k method and band-pass filtering, and critically sampled Discrete Wavelet Transforms. Final results showed that the DTCWT is reliable in comparison with other methods for ground roll attenuation.

A STATISTICAL SHARPNESS MEASURE BASED MULTI FOCUS IMAGE FUSION USING DOUBLE DENSITY DISCRETE WAVELET TRANSFORM

Image fusion is the process of combining two or more images of the same scene to form the fused image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. Transform fusion uses transform for representing the source image at multi scale. Due to the compactness, orthogonality and directional information, the Discrete Wavelet Transforms and its undecimated version are used for image fusion. These transforms can be implemented using perfect reconstruction Finite Impulse Response filter banks which are either symmetric or orthogonal. To design filters to have both symmetric and orthogonal properties, the number of filters is increased to generate M-band transform. Double density Discrete Wavelet Transform is an example of M-band DWT and consists of one scaling and two wavelet filters. In this paper, an approach for DDWT based image fusion is designed using statistical property of wavelet filters in representing the sharpness and its performance is measured in terms of Root Mean Square Error, Peak to Signal Noise Ratio, Quality Index.

A Hybrid Model for Secure Data Transfer in Audio Signals using HCNN and DD DWT

In today’s world, there are a number of cryptographic and steganography techniques used in order to have secured data transfer between a sender and a receiver. In this paper a new hybrid approach that integrates the merits of cryptography and audio steganography is presented. First, the original message is encrypted using chaotic neural network and the resultant cipher text is embedded into a cover audio using Double Density Discrete Wavelet Transform (DD DWT). The resultant stego audio is transmitted to the receiver and the reverse process is done in order to get back the original plain text. The proposed method presents a Steganography scheme along with the cryptography scheme which enhances the security of the algorithm.

Seismic coherent and random noise attenuation using the undecimated discrete wavelet transform method with WDGA technique

One of the most crucial challenges in seismic data processing is the reduction of the noise in the data or improving the signal-to-noise ratio. In this study, the 1D undecimated discrete wavelet transform (UDWT) has been acquired to attenuate random noise and ground roll. Wavelet domain ground roll analysis (WDGA) is applied to find the ground roll energy in the wavelet domain. The WDGA will be a substitute method for thresholding in seismic data processing. To compare the effectiveness of the WDGA method, we apply the 1D double density discrete wavelet transform (DDDWT) using soft thresholding in the random noise reduction and ground roll attenuation processes. Seismic signals intersect with ground roll in the time and frequency domains. Random noise and ground roll have many undesirable effects on pre-stack seismic data, and result in an inaccurate velocity analysis for NMO correction. In this paper, the UDWT by using the WDGA technique and DDDWT (using the soft thresholding technique) and the regular Fourier based method as f?k transform will be used and compared for seismic denoising.

방향의 선택성 향상을 통한 이중 밀도 이산 웨이브렛 변환의 성능 개선

The double-density discrete wavelet transform(DWT) is an improvement upon the critically sampled DWT with important additional properties. It employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. And it is overcomplete by a factor of two. Also, this transformation is nearly shift-invariant. But there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. Proposed method is a DWT that combines the double-density DWT and quincunx sampling, each of which has its own characteristics and advantages. Especially, the quincunx sampling treats the different directions more homogeneously. As a result, since proposed method can generate sub-images of multiple degrees rotated versions, this method provides an improved performance in image processing fields.

QRS COMPLEX DETECTION USING DOUBLE DENSITY DISCRETE WAVELET TRANSFORM

In this paper, an offline double density discrete wavelet transform based QRS complex detection of the electrocardiogram signal is discussed. Baseline wandering present in the signal is removed by using the double density discrete wavelet transformed approximation coefficients of the signal. The results are more accurate than other methods with less effort. This is an unsupervised method allowing the process to be used in offline automatic analysis of electrocardiogram. The measurement of timing intervals of ECG signal by automated system is highly superior to its subjective analysis. The heart rate signals are essentially non-stationary and contain indicators of current disease or warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. Double density discrete wavelet transform is easier to implement, provides multiresolution and also reduces the computational time. In the pre-processing step, the baseline wandering is removed from the ECG signal. Then the R peaks/QRS complexes are detected. From the location of the R peaks, the successive RR intervals and heart rate are calculated. Fifty-two records from the MIT-BIH arrhythmia database are used to evaluate the proposed method. Sensitivity and positive prediction are used as performance measures. This method detects the R peaks with 100% sensitivity and 99.95% positive prediction. The performance of the proposed method is better than other methods existing in the literature.

DE-NOISING AND BASELINE WANDERING REMOVAL OF ELECTROCARDIOGRAM USING DOUBLE DENSITY DISCRETE WAVELET

In this paper, an off-line double density discrete wavelet transform based de-noising and baseline wandering removal methods are proposed. Different levels decomposition is used depending upon the noise level, so as to give a better result. When the noise level is low, three levels decomposition is used. When the noise level is medium, four levels decomposition is used. When the noise level is high, five levels decomposition is used. Soft threshold technique is applied to each set of wavelet detail coefficients with different noise level. Donoho's estimator is used as a threshold for each set of wavelet detail coefficients. The results are compared with other classical filters and improvement of signal to noise ratio is discussed. Using the proposed method the output signal to noise ratio is 19.7628 dB for an input signal to noise ratio of -7.11 dB. This is much higher than other methods available in the literature. Baseline wandering removal is done by using double density discrete wavelet approximation coefficients of the whole signal. This is an unsupervised method allowing the process to be used in off-line automatic analysis of electrocardiogram. The results are more accurate than other methods with less effort.