A high-throughput VLSI design for JPEG2000 9/7 discrete wavelet transform

To achieve high parallel computation of discrete wavelet transform （DWT） in JPEG2000, a high-throughput two-dimensional （2D） 9/7 DWT very large scale integration （VLSI） design is proposed, in which the row processor is based on flipping structure. Due to the difference of the input data flow, the column processor is obtained by adding the input selector and data buffer to the row processor. Normalization steps in row and column DWT are combined to reduce the number of multipliers, and the rationality is verified. By rearranging the output of four-line row DWT with a multiplexer （MUX）, the amount of data processed by each column processor becomes half, and the four-input/four- output architecture is implemented. For an image with the size of N x N, the computing time of one-level 2D 9/7 DWT is 0.25N2 ＋ 1.5N clock cycles. The critical path delay is one multiplier delay, and only 5N internal memory is required. The results of post-route simulation on FPGA show that clock frequency reaches 136 MHz, and the throughput is 544 Msample/s, which satisfies the requirements of high-speed applications.

Interpolation of Images Using Discrete Wavelet Transform to Simulate Image Resizing as in Human Vision

This paper presents discrete wavelet transform （DWT） and its inverse （IDWT） with Haar wavelets as tools to compute the variable size interpolated versions of an image at optimum computational load. As a human observer moves closer to or farther from a scene, the retinal image of the scene zooms in or out, respectively. This zooming in or out can be modeled using variable scale interpolation. The paper proposes a novel way of applying DWT and IDWT in a piecewise manner by non-uniform down- or up-sampling of the images to achieve partially sampled versions of the images. The partially sampled versions are then aggregated to achieve the final variable scale interpolated images. The non-uniform down- or up-sampling here is a function of the required scale of interpolation. Appropriate zero padding is used to make the images suitable for the required non-uniform sampling and the subsequent interpolation to the required scale. The concept of zeroeth level DWT is introduced here, which works as the basis for interpolating the images to achieve bigger size than the original one. The main emphasis here is on the computation of variable size images at less computational load, without compromise of quality of images. The interpolated images to different sizes and the reconstructed images are benchmarked using the statistical parameters and visual comparison. It has been found that the proposed approach performs better as compared to bilinear and bicubic interpolation techniques.

A Robust Image Watermarking Scheme Using Z-Transform, Discrete Wavelet Transform and Bidiagonal Singular Value Decomposition

Watermarking is a widely used solution to the problems of authentication and copyright protection of digital media especially for images,videos,and audio data.Chaos is one of the emerging techniques adopted in image watermarking schemes due to its intrinsic cryptographic properties.This paper proposes a new chaotic hybrid watermarking method combining Discrete Wavelet Transform(DWT),Z-transform(ZT)and Bidiagonal Singular Value Decomposition(BSVD).The original image is decomposed into 3-level DWT,and then,ZT is applied on the HH3 and HL3 sub-bands.The watermark image is encrypted using Arnold Cat Map.BSVD for the watermark and transformed original image were computed,and the watermark was embedded by modifying singular values of the host image with the singular values of the watermark image.Robustness of the proposed scheme was examined using standard test images and assessed against common signal processing and geometric attacks.Experiments indicated that the proposed method is transparent and highly robust.

FAST TEXT LOCATION BASED ON DISCRETE WAVELET TRANSFORM

The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective.

AN EFFICIENT 3-DIMENSIONAL DISCRETE WAVELET TRANSFORM ARCHITECTURE FOR VIDEO PROCESSING APPLICATION

This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized by applying block enabling technique, scaling, and rounding of the filter coefficients. The proposed architecture uses biorthogonal (9/7) wavelet filter. The architecture is modeled using Verilog HDL, simulated using ModelSim, synthesized using Xilinx ISE and finally implemented on Virtex-5 FPGA. The proposed 3-DDWT architecture has slice register utilization of 5%, operating frequency of 396 MHz and a power consumption of 0.45 W.

Comparison of fast discrete wavelet transform algorithms

This paper presents an analysis on and experimental comparison of several typical fast algorithms for discrete wavelet transform (DWT) and their implementation in image compression, particularly the Mallat algorithm, FFT-based algorithm, Short- length based algorithm and Lifting algorithm. The principles, structures and computational complexity of these algorithms are explored in details respectively. The results of the experiments for comparison are consistent to those simulated by MATLAB. It is found that there are limitations in the implementation of DWT. Some algorithms are workable only for special wavelet transform, lacking in generality. Above all, the speed of wavelet transform, as the governing element to the speed of image processing, is in fact the retarding factor for real-time image processing.

A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.

Robust stability test for 2-D continuous-discrete systems with interval parameters

It is revealed that the dynamic stability of 2-D recursive continuous-discrete systems with interval parameters involves the problem of robust Hurwitz-Schur stability of bivariate polynomials family. It is proved that the Hurwitz-Schur stability of the denominator polynomials of the systems is necessary and sufficient for the asymptotic stability of the 2-D hybrid systems. The 2-D hybrid transformation, i. e. 2-D Laplace-Z transformation, has been proposed to solve the stability analysis of the 2-D continuous-discrete systems, to get the 2-D hybrid transfer functions of the systems. The edge test for the Hurwitz-Schur stability of interval bivariate polynomials is introduced. The Hurwitz-Schur stability of the interval family of 2-D polynomials can be guaranteed by the stability of its finite edge polynomials of the family. An algorithm about the stability test of edge polynomials is given.

基于2D-DWT与FLD的人脸识别算法

基于加权二维离散小波变换(2D-DWT)与Fisher线性判别(FLD),提出一种人脸识别算法。利用db2小波对人脸图像进行2层小波分解,对于分解图像利用FLD法进行特征提取,运用最近邻分类法对提取的特征进行分类识别。在ORL标准人脸图像库上的实验结果证明,该算法能取得较好的识别率。

结合2DWT和Fisherfaces的人脸特征提取新方法

针对人脸表情变化对人脸识别的影响,提出一种结合小波变换(DWT)、特征脸方法(PCA)和线性判别法(LDA)的人脸特征提取新方法。首先将人脸图像通过二维小波变换(2DWT)提取其低频分量,然后将低频图像经过PCA变换映射到一个低维空间,最后在低维空间中利用LDA方法进行人脸特征的提取。通过此方法,采用ORL人脸库和Yale人脸库进行测试,我们可实现更准确的特征提取,并有效解决表情变化对人脸识别的影响问题。实验结果显示,本文方法在提高人脸识别率的同时,也提高了人脸识别速度。

基于DWT-SARIMA-LSTM的流感预测模型研究

为提高流感预测模型的准确率,针对流感数据的季节性与波动性特点,提出利用离散小波分解(DWT)、季节性自回归综合移动平均模型(SARIMA)和长短期记忆神经网络(LSTM)综合建模,构建DWT-SARIMA-LSTM混合预测模型。首先,将流感数据分解为高频成分与低频成分,对低频成分使用SARIMA模型、高频成分使用LSTM模型分别进行预测;其次,将预测值融合得到最终的预测结果;最后,构建流行控制图预警模型。使用从中国香港卫生署官网获得的中国香港地区2010—2019年的流感数据对模型进行预测和验证,其MAE为0.3427,MAPE为8.0973%,RMSE为0.4632,预警模型的准确率为100%,该模型较于如ARIMA-LSTM等其他混合模型有更高的预测精度。

基于DWT-VMD混合信号分解技术的人体活动识别

在人类活动识别的应用环境中,从原始传感器数据中提取更加有效的特征仍具有挑战性。针对该问题,利用离散小波变换(Discrete wavelet transform,DWT)和变分模式分解(Variational mode decomposition,VMD)的混合信号分解技术提取原始信号中的显著特征向量。在UCI-HAR数据集与SCUT-NAA数据集上,利用多种机器学习分类算法,例如K近邻、随机森林、LightGBM和XGBoost,对DWT-VMD混合信号分解算法的有效性进行了实验。实验结果表明,与未使用混合信号分解技术相比,使用该技术后识别准确率均有所提高,其中UCI-HAR数据集分类准确率达到98.91%,与未加入分解算法相比提高了1.79%;SCUT-NAA数据集分类准确率达到95.52%,提高了3.2%。在人体活动识别中,利用DWT-VMD混合信号分解技术,能够提取原始信号中更有效的特征,提高识别准确率,具有一定的实用性。

雷达图像压缩中2D-DWT的FPGA设计

首先介绍小波变换(WT)理论,分析图像压缩的基本原理。结合雷达图像数据压缩的特点,着眼于系统的实时性最佳性能,提出FPGA硬件实现算法最优解决方案,详细叙述了FPGA设计总体结构,一维滤波器组设计,行列滤波数据缓存等关键算法的实现。

JPEG2000中DWT的MATLAB实现

随着多媒体技术应用的不断增加,图像压缩技术不仅要求具有较高的压缩性能,而且还要求有新的特征来满足一些特殊要求.为此,国际标准化组织(ISO)制定了新一代静止图像压缩标准:JPEG2000.通过对JPEG2000中的离散小波变换(DWT)的详细分析,给出了用MATLAB6. 5实现其核心变换的方法,并利用小波变换工具箱对各种图像进行压缩后,给出了小波基的选择方法.

A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

Wavelet Multiview-Based Hybrid Deep Learning Model for Forecasting El Niño-Southern Oscillation Cycles

The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.

Fault Feature Extraction of Rotating Machinery Based on Wavelet Transformation and Multi-resolution Analysis

This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.

A Watermarking Algorithm Based on Wavelet and Hadamard Transform for Color Image

Digital image watermarking is a useful solution to the problem of information security, copyright and network security. In this paper, we propose a watermarking algorithm for color image based HT and DWT. A binary image as watermark is embedded into green component or blue component of color image. The algorithm can satisfy the transparence and robustness of the watermarking system very well. The experiment based on this algorithm demonstrates that the watermarking is robust to the common signal processing techniques including JPEG compressing, adding noise, low pass filter, and mosaic.

基于CWT和DWT相结合的谐波检测

提出了一种基于连续小波变换(CWT)和离散小波变换(DWT)相结合的电力系统谐波检测方法。首先利用CWT系数的幅值来检测谐波频率,该过程不用事先根据谐波次数确定分解层数,而只是确定尺度范围及步长,即可得出各次谐波频率。然后根据确定的谐波成分利用DWT来检测谐波幅值,并通过Matlab软件进行了仿真分析。仿真结果表明该方法有效地解决了基于离散小波变换的谐波检测方法中谐波次数未知而无法确定分解层数的难题,并能精确可靠检测各次谐波频率和相应的幅值。因此,CWT和DWT相结合是一种有效的电力系统谐波检测方法。

基于DWT的3D点云模型数字水印算法

提出一种基于离散小波变换(DWT)的3D点云模型数字水印算法。选取3D点云模型各点到模型重心距离值的一维DWT低频信号部分,进行水印信息的嵌入。实验结果表明,该算法嵌入数据量大,算法简单且容易实现,同时对仿射变换、重排序攻击、简化、剪切和噪声等变形攻击具有较强的鲁棒性。