Three-dimensional Discrete Wavelet Research Articles

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.

Component mode decomposition using three-dimensional discrete wavelet transform and its application to relevance evaluation of characteristic modes

Component mode decomposition using three-dimensional discrete wavelet transform and its application to relevance evaluation of characteristic modes

A Proposed of Multimedia Compression System Using Three - Dimensional Transformation

Video compression has become especially important nowadays with the increase of data transmitted over transmission channels, the reducing the size of the videos must be done without affecting the quality of the video. This process is done by cutting the video thread into frames of specific lengths and converting them into a three-dimensional matrix. The proposed compression scheme uses the traditional red-green-blue color space representation and applies a three-dimensional discrete Fourier transform (3D-DFT) or three-dimensional discrete wavelet transform (3D-DWT) to the signal matrix after converted the video stream to three-dimensional matrices. The resulting coefficients from the transformation are encoded using the EZW encoder algorithm. Three main criteria by which the performance of the proposed video compression system will be tested; Compression ratio (CR), peak signal-to-noise ratio (PSNR) and processing time (PT). Experiments showed high compression efficiency for videos using the proposed technique with the required bit rate, the best bit rate for traditional video compression. 3D discrete wavelet conversion has a high frame rate with natural spatial resolution and scalability through visual and spatial resolution Beside the quality and other advantages when compared to current conventional systems in complexity, low power, high throughput, low latency and minimum the storage requirements. All proposed systems implement using MATLAB R2020b.

3D-DWT cross-band statistics and features for No-Reference Video Quality Assessment (NR-VQA)

This paper presents a robust, novel, and computationally efficient noise estimation-based NR-VQA model. It uses four novel sub-band features; namely cross-band statistics, sub-band kurtosis, sub-band energy ratios, and natural video statistics in three-dimensional discrete wavelet transform (3D-DWT) domain. As the HVS shows high sensitivity towards the structural and textural information, a three-dimensional gray level co-occurrence matrix (3D-GLCM) is used to select top 25% video blocks having maximal textural information. We validate the performance of the proposed method on five publicly available and widely used video quality databases. Extensive experiments show that the proposed model yields considerably better and robust performance than other contemporary state-of-the-art NR-VQA methods at lower computational costs.

Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform

Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty of this work is to bring out the features of 3D-DWT applicable to hyperspectral images classification using Haar, Fejér-Korovkin and Coiflet filters. Three-dimensional-DWT is implemented with the help of three stages of 1D-DWT. The first two stages of 3D-DWT are extracting spatial resolution, and the third stage is extracting the spectral content. In this work, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) K-nearest neighbor (KNN) and (iii) support vector machine (SVM). Exploiting both spectral and spatial features help the classifiers to provide a better classification accuracy. A comparison of results was performed with the same classifiers without DWT features. The experiments were performed using Salinas Scene and Indian Pines hyperspectral datasets. From the experiments, it has been observed that the SVM with 3D-DWT features performs better in terms of the performance metrics such as overall accuracy, average accuracy and kappa coefficient. It has shown significant improvement compared to the state of art techniques. The overall accuracy of 3D-DWT+SVM is 88.3%, which is 14.5% larger than that of traditional SVM (77.1%) for the Indian Pines dataset. The classification map of 3D-DWT + SVM is more closely related to the ground truth map.

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Interest in facial recognition hypotheses and algorithms has grown steadily over the last few decades. Video monitoring, criminal identification, building access control, and unmanned and autonomous vehicles are only a few examples of concrete applications that are becoming increasingly attractive to industry. Various techniques are being developed, including local, holistic, and hybrid approaches, which use only a few face image characteristics or the entire facial features to provide a face image description. Many methods have good results, if there are sufficiently representative training samples per person, in the face recognition system. Facial part finding and extraction show the utmost vital role in face and age recognition. In this research work a new algorithm is proposed for Face and Age Recognition (FAR) by using Discrete Wavelet Transform (DWT), Radial Basis Function Support Vector Machine (RBF-SVM) classifier, and Rotational Local Binary Pattern (RLBP). RLBP is utilized for the selection and extraction of features from the face image. In this algorithm, extract the face component like Nose, Mouth, Left and Right eye. In the preprocessing stage median filter is used to remove noises from the face image. By using this, there is an improvement in the feature extraction procedure. In pattern recognition, a basic errand is finding a picture from the picture parts. For the implementation of results FG-NET ((Face and Gesture Recognition Network) and AT&T datasets are used. The detection rate of face recognition has reached up to 92–98% and the detection rate for age recognition is 87%. The proposed algorithm is compared with SVM shows better over previous algorithms and also estimate the value of accuracy.

Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet Transform and Markov Random Field

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.

3D discrete wavelet transform‐based feature extraction for hyperspectral face recognition

Facial hyperspectral image analysis has become a popular topic since it provides additional spectral information on subjects unlike the 2D face imagery which only has spatial information, hence it has an opportunity to improve face recognition accuracy. Three new methods for feature extraction for facial hyperspectral image classification are proposed. The methods employ a three-dimensional discrete wavelet transform (3D-DWT) to extract features from facial hyperspectral images. One of the advantages of 3D-DWT for feature extraction in hyperspectral images is that the horizontal, vertical and spectral information are processed in parallel. The most important characteristic of 3D-DWT is decomposing hyperspectral images into a set of spatio-spectral frequency subbands. The study proposes three methods using 3D-DWT for feature extraction: 3D-subband energy, 3D-subband overlapping cube and 3D-global energy. The k-NN and collaborative representation-based classifier (CRC) are used to process extracted feature vector datasets, where classification accuracies are evaluated by four test scenarios. The results under different test scenarios revealed that accuracy of proposed 3D-DWT methods is superior to alternative methods using spatio-spectral classification.

A novel framework for compressed sensing based scalable video coding

Considering high throughput values as specified by modern video processing standards, Scalable Video Coding (SVC) systems intended for such standards are generally implemented by means of dedicated hardware. However, the high computational complexity associated with the current Compressed Sensing (CS) based video coding schemes makes their hardware realization considerably challenging. In this paper, we present a novel CS based SVC framework that is amenable to real-time VLSI implementation. At the encoder, after applying the Three-Dimensional Discrete Wavelet Transform (3-D DWT) on the input video frames, a novel Adaptive Measurement Scheme (AMS) in CS is introduced, which is applied on the high frequency sub-bands of the 3-D DWT frames. The proposed AMS along with 3-D DWT not only achieves scalability and better compression ratio, but also reduces the overall computational complexity of the system. We have also proposed an Enhanced Approximate Message Passing (EAMP) algorithm to reconstruct the high frequency sub-bands from the CS measurements at the decoder. The proposed EAMP procedure combines the benefits of Approximate Message Passing (AMP) and Iterative Hard Thresholding (IHT) algorithms thereby simultaneously achieving sparsity measurement trade-off and good reconstruction quality. We have carried out the detailed complexity analysis and simulations to demonstrate the superiority of the proposed framework over the existing schemes.

A new denoising method for fMRI based on weighted three-dimensional wavelet transform

This study presents a new three-dimensional discrete wavelet transform (3D-DWT)-based denoising method for functional magnetic resonance images (fMRI). This method is called weighted three-dimensional discrete wavelet transform (w-3D-DWT), and it is based on the principle of weighting the volume subbands which are obtained by 3D-DWT. Briefly, classical DWT denoising consists of wavelet decomposition, thresholding, and image reconstruction steps. In the thresholding algorithm, the thresholding value for each image cannot be chosen exclusively. Namely, a specific thresholding value is chosen and it is used for all images. The proposed algorithm in this study can be considered as a data-driven denoising model for fMRI. It consists of three-dimensional wavelet decomposition, subband weighting, and image reconstruction. The purposes of subband weighting algorithm are to increase the effect of the subband which represents the image better and to decrease the effect of the subband which represents the image in the worst way and thus to reduce the noises of the image adaptively. fMRI is one of the popular methods used to understand brain functions which are often corrupted by noises from various sources. The traditional denoising method used in fMRI is smoothing images with a Gaussian kernel. This study suggests an adaptive approach for fMRI filtering different from Gaussian smoothing and 3D-DWT thresholding. In this study, w-3D-DWT denoising results were evaluated with mean-square error (MSE), peak signal/noise ratio (PSNR), and structural similarity (SSIM) metrics, and the results were compared with Gaussian smoothing and 3D-DWT thresholding methods. According to this comparison, w-3D-DWT gave low-MSE and high-PSNR results for fMRI data.

A New Zero-Watermarking Algorithm Resisting Attacks Based on Differences Hashing

Abstract Medical volume data containing patient information is often faced with various attacks in the transmission process. In order to enhance the medical information system security, and effectively solve the problem of medical volume data protection, a new zero-watermarking algorithm is proposed in the paper. The new zero-watermarking algorithm takes advantage of three-dimensional discrete wavelet transform multi-resolution analysis characteristics of space and time, threedimensional discrete cosine transform properties, and differences hashing robust characteristic. In order to enhance watermarking algorithm security, Legendre chaotic neural network is used for scrambling original watermark image. The medical volume data is made by three-dimensional discrete wavelet transform, threedimensional discrete cosine transform and three-dimensional discrete inverse cosine transform r to obtain the medical volume data feature matrix (4×5×4), which is converted to 64-bit binary feature sequence through difference hashing algorithm. The 64-bit binary feature sequence is used to construct the zero-watermarking. The experimental results prove that the new zero-watermarking has favorable security and robustness resisting various attacks. Therefore, the new zero-watermarking algorithm is more applicable to protect medical volume data.

Background Subtraction Based on Three-Dimensional Discrete Wavelet Transform.

Background subtraction without a separate training phase has become a critical task, because a sufficiently long and clean training sequence is usually unavailable, and people generally thirst for immediate detection results from the first frame of a video. Without a training phase, we propose a background subtraction method based on three-dimensional (3D) discrete wavelet transform (DWT). Static backgrounds with few variations along the time axis are characterized by intensity temporal consistency in the 3D space-time domain and, hence, correspond to low-frequency components in the 3D frequency domain. Enlightened by this, we eliminate low-frequency components that correspond to static backgrounds using the 3D DWT in order to extract moving objects. Owing to the multiscale analysis property of the 3D DWT, the elimination of low-frequency components in sub-bands of the 3D DWT is equivalent to performing a pyramidal 3D filter. This 3D filter brings advantages to our method in reserving the inner parts of detected objects and reducing the ringing around object boundaries. Moreover, we make use of wavelet shrinkage to remove disturbance of intensity temporal consistency and introduce an adaptive threshold based on the entropy of the histogram to obtain optimal detection results. Experimental results show that our method works effectively in situations lacking training opportunities and outperforms several popular techniques.

Zero-watermarking Algorithm for Medical Volume Data Based on Legendre Chaotic Neural Network and Perceptual Hashing

Medical information digitization makes the medical information storage and extraction more convenient. Medical image information security and copyright protection is also gradually being taken seriously, and some medical image watermarking has been applied. According to the characteristics of three-dimensional medical images, this paper proposes a robust zero-watermarking algorithm for medical volume data based on legendre chaotic neural network and perceptual hashing. The algorithm is based on three-dimensional discrete wavelet transform frequency analysis features, which uses perceptual hashing technique to extract medical volume data itself feature vector in order to structure robust zero watermarking. And using legendre chaotic neural network to generate chaotic sequence to enhance the security of the watermarking. The algorithm achieves a combination of legendre chaotic neural network encryption and zerowatermarking technology, which can improve the medical volume data watermarking algorithm security and robustness. The simulation results show the effectiveness of the algorithm.

A New Robust Zero-watermarking Algorithm for Medical Volume Data

Aiming at medical image information security problem, this paper proposes a new robust zero-watermarking algorithm for medical volume data based on three-dimensional discrete wavelet transform, three-dimensional Fourier transform and hermite chaotic neural network . Firstly, it employs a novel hermite chaotic neural network to generate the pseudo-random chaotic sequence for scrambling. Secondly, three dimensional medical image is transformed by three-dimensional discrete wavelet transform and three-dimensional discrete Fourier transform. Then, select the transformed low and intermediate frequency coefficients symbol as medical volume data characteristics to structure zero-watermarking. The algorithm integrates hermite chaotic neural network and zero-watermarking technology, which is not confined to artificially selected region of interest. The watermarking extraction does not need the original image. And its security depends on the chaotic sequence complexity and unpredictability, solving the watermark embedding, extraction safety and efficiency. The simulation results show that the algorithm is simple to implement, with good robustness, security and invisibility.

A Low-Complexity Bit-Plane Entropy Coding and Rate Control for 3-D DWT Based Video Coding

This paper is dedicated to fast video coding based on three-dimensional discrete wavelet transform. First, we propose a novel low-complexity bit-plane entropy coding of wavelet subbands based on Levenstein zero-run coder for low entropy contexts and adaptive binary range coder for other contexts. Second, we propose a rate-distortion efficient criterion for skipping 2-D wavelet transforms and entropy encoding based on parent-child subband tree. Finally, we propose one pass rate control which uses virtual buffer concept for adaptive Lagrange multiplier selection. Simulations results show that the proposed video codec has a much lower computational complexity (from 2 to 6 times) for the same quality level compared to the H.264/AVC standard in the low complexity mode.

Video denoising in three-dimensional complex wavelet domain using a doubly stochastic modelling

This study presents a new video denoising method in the three-dimensional (3D) discrete complex wavelet transform (DCWT) domain. The authors assume that the coefficients have zero mean and Gaussian local distributions given the unknown variances. In practice, the locally estimated variances (LEVs) are not accurate and are simply maximum-likelihood estimates from the conditional Gaussian distribution. To take into account the inaccuracies of LEVs and motivated by experiments, the authors assume that the LEVs have gamma distributions. This is equivalent to the unconditional heavy-tailed local Bessel K-form prior densities given LEVs. This model is able to more accurately model the intrascale dependency between adjacent wavelet coefficients. The authors employ both maximum a posteriori and minimum mean-squared error MMSE estimators of the unconditional distributions, to reduce the noise in the 3D DCWT domain. The authors examine their spatially adaptive algorithm for reduction of various types of noise including additive white Gaussian noise, non-stationary noise, Poisson noise and speckle noise. The proposed method results in an impressive video enhancement without any explicit use of motion estimation. This is because, the 3D DCWT is a motion selective transform and isolates the motions and directions in its sub-bands.

Wavelet analysis of the particle distribution in a down-flow fluidized bed based on electrical capacitance tomography images

In the present paper, the extraction of dominant particle concentration fluctuation at various time–space frequency levels from reconstructed electrical capacitance tomography (ECT) images by means of the discrete wavelet transform has been proposed. Using this concept, three-dimensional images (time and two-dimensional space) obtained by an ECT system are decomposed into different time and space levels to characterize the intrinsic features of the particle distribution in a down-flow fluidized bed with a newly designed distributor using three-dimensional discrete wavelet multiresolution. As a result, the dominant time and space particle distribution characteristics for a specific frequency level can be visualized. On the basis of the wavelet analysis results, the best operation condition, under which the most stable and uniform particle distribution can be obtained, was clarified. This provides a good reference for the optimization of a downer reactor to qualify the refinery process in the petroleum industry.

Video scene analysis in 3D wavelet transform domain

This paper proposes a novel scene analysis algorithm based on three-dimensional discrete wavelet transform (3D DWT). Based on the correlation among the adjacent frames, video frames can be considered as four categories: abrupt scene transition, motion scene, gradual scene transition and static scene, which are ranked from low to high according to the strength of the correlation. Through the investigation of the particular temporal and spatial distribution of each category, the correlation among adjacent frames could be described by the 3D DWT coefficients related statistical features, which are the energy of high-frequency coefficients difference, the sum of high-frequency coefficients magnitudes and the difference of low-frequency coefficients magnitudes. The energy of high-frequency coefficients difference is first used to detect the abrupt scene transition including cut and flashlight. Then all the three features are input to SVM for the purpose of analyzing the residual scenes and detecting the gradual scene transition, such as dissolve and fade. Experimental results show the method to be effective not only for the abrupt scene transition detection, but also for the gradual scene transition detection.

Texture Compression and Relighting of 3-D Color Objects Using Singular Value Decomposition

Synthesis of 3-D objects is essential to animation, computer games and virtual reality. This work presents a novel view-dependent mapping method by taking the advantages of image-based and model-based rendering techniques. The proposed approach utilizes singular value decomposition to compute the coefficient matrices of the data captured from different viewing directions and different illuminations. These coefficient matrices are then compressed by the three-dimensional discrete wavelet transform. The remaining data are further coded via quantization and entropy coding to minimize the size for storage. We can generate the appearance of an object from a new viewing direction under a new light source. Experimental results evaluate the performance by testing different materials and demonstrate the effectiveness of the proposed approach.

Error-Resilient 3-D Wavelet Video Coding with Duplicated Lowest Sub-Band Coefficients and Two-Step Error Concealment Method

In this paper, we propose a two-step error concealment algorithm based on an error resilient three-dimensional discrete wavelet transform (3-D DWT) video coding scheme. The proposed scheme consists of an error-resilient encoder duplicating the lowest sub-band bit-streams for dispersive grouped frames and an error concealment decoder. The error concealment method of this decoder is decomposed of two steps, the first step is replacement of erroneous coefficients in the lowest sub-band by the duplicated coefficients, and the second step is interpolation of the missing wavelet coefficients by minimum mean square error (MMSE) estimation. The proposed scheme can achieve robust transmission over unreliable channels. Experimental results provide performance comparisons in terms of peak signal-to-noise ratio (PSNR) and demonstrate increased performances compared to state-of-the-art error concealment schemes.