检索结果-内蒙古大学图书馆

International Journal of wavelets, Multiresolution and Information processing

International Journal of wavelets, Multiresolution and Information processing

International Journal of wavelets, Multiresolution and Information processing (hereafter referred to as IJWMIP) is a bi-monthly publication for theoretical and applied papers on the current state-of-the-art results of wavelet analysis, multiresolution and information processing. Papers related to the IJWMIP theme are especially solicited, including theories, methodologies, algorithms and emerging applications. Topics of interest of the IJWMIP include, but are not limited to: 1. wavelets: wavelets and operator theory Frame and applications Time-frequency analysis and applications Sparse representation and approximation Sampling theory and compressive sensing wavelet based algorithms and applications 2. Multiresolution: Multiresolution analysis Multiscale approximation Multiresolution image processing and signal processing Multiresolution representations Deep learning and neural networks Machine learning theory, algorithms and applications High dimensional data analysis 3. Information processing: Data sciences Big data and applications Information theory Information systems and technology Information security Information learning and processing Artificial intelligence and pattern recognition image/signal processing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Design and vLSI Implementation of Symmetric Daubechies wavelet Filter Bank for image processing applications

引用

CIRCUITS SYSTEMS AND signal processing 2025年第5期44卷 3625-3640页

作者： Samantaray, Aswini K. Rahulkar, Amol D. Sahoo, Satyajeet Gorre, Pradeep Manipal Inst Technol Bengaluru Manipal Inst Higher Educ Dept ECE Manipal India Natl Inst Technol Dept EEE Veroda Goa India Vignans Fdn Sci Technol & Res Dept ECE Guntur India

Due to the irrational coefficients, the orthogonal wavelet filter banks (FBs) need a lot of resources when implemented on hardware. As a result, there is a decrease in operating speed, a significant memory requirement, and an increase in power consumption. This paper proposes an orthogonal low complex symmetric Daubechies-2 (4-tap) wavelet filter bank (FB) to address these problems. This is accomplished by obtaining dyadic filter coefficients and making the suggested FB symmetric by marginally modifying the perfect reconstruction (PR) criterion. By using fewer adders and shifters, the suggested wavelet FB achieves a significant reduction in dynamic power consumption without the need for multipliers. This is confirmed by implementing the suggested wavelet FB on the Zedboard ZYNQ-7000 AP-SoC (Zynq FPGA from Xilinx) field programmable gate array (FPGA). The suitability of the suggested FB is tested in medical image retrieval and image compression applications. Results from simulations demonstrate that, when compared to state-of-the-art techniques, the suggested wavelet FB performs better in terms of retrieval accuracy (ARP, ARR) for medical image retrieval and PSNR for image compression on the benchmark image datasets.

关键词： Orthogonal wavelets Perfect reconstruction Field programmable gate array image compression image retrieval

来源：评论

学校读者我要写书评

暂无评论

Improved DWT and IDWT architectures for image compression

引用

MICROPROCESSORS AND MICROSYSTEMS 2024年 104卷

作者： Chowdhury, Ritesh Sur Jana, Jhilam Tripathi, Sayan Bhaumik, Jaydeb Jadavpur Univ Dept ETCE Kolkata India

In the recent era, a rapid development in the field of image processing has been observed. One of the important applications in image processing is compression. Several wavelet transform based image compression techniques have already been introduced. In this paper, Discrete wavelet Transform (DWT) and Inverse Discrete wavelet Transform (IDWT) based improved image compression and decompression techniques have been proposed by incorporating a scaling factor. The DWT and IDWT algorithms are implemented using folded architecture. To reduce the usages of hardware resources, a multiplier is recursively used. image compression and decompression schemes based on proposed DWT and IDWT architectures are tested using four different image databases. The proposed technique provides better results in terms of bits per pixel, compression ratio, mean square error, peak-signal-to-noise ratio, normalized correlation coefficient and structural similarity index. FPGA based synthesis has been performed using Xilinx vivado Synthesis tool in terms of slice LUTs, slice registers, clock frequency, delay and power. The synthesis results show that proposed DWT and IDWT architectures are amenable for image compression and decompression applications.

关键词： image compression Discrete wavelet Transform Daubechis-4 wavelet Huffman code FPGA

来源：评论

学校读者我要写书评

暂无评论

Multi-image fusion: optimal decomposition strategy with heuristic-assisted non-subsampled shearlet transform for multimodal image fusion

引用

signal image AND vIDEO processing 2024年第3期18卷 2297-2307页

作者： Ravi, Jampani Subbayamma, B. v. Kumar, P. vijaya Sriramam, Yadavalli S. S. Marlin, S. Sheeba, Adlin Kumar, N. v. Phani Sai SRKR Engn Coll Dept ECE Bhimavaram AP India Prasad V Potluri Siddhartha Inst Technol Dept ECE Vijayawada AP India Aditya Engn Coll Dept ECE Surampalem AP India Agni Coll Technol Dept EEE Chennai India St Josephs Inst Technol Dept Comp Sci & Engn Chennai India

image fusion is significant in various distinct sectors of image processing, from remote sensing to medical applications. In recent years, real-valued wavelet transforms have been utilized to fuse images. This approach has offered enhancements against various poor approaches;however, this task lacks the shift variance and suffers from the directionality connected with its wavelet bases. Moreover, the conventional architecture of this hard wavelet decomposition implements a very hard resolution of filters to attain an essential quarter shift in the coefficient result. The establishment of image fusion methodology is to provide data integrated from distinct images to avoid inconsistency and redundancy presented among the images. This approach is utilized to enhance the utilization, reliability, accuracy, and interpretation of the data with the development of image data transparency by creating an accurate and clear detail of the monitored target. In this research work, a transform-aided image fusion mechanism is utilized to enhance the effectiveness in a better way. With the support of this approach, good "peak signal-to-noise ratio (PSNR)" with a minimum "mean square error (MSE)" can be achieved. Therefore, this work is aimed to implement a new multi-image fusion approach by fusing the normal images. Initially, the standard normal images are manually collected for the approach. Then, the decomposition of two images in the same scene is done through "optimal non-subsampled shearlet transform (ONSST)," where the attributes of NSST are optimized with the help of recommended fitness improved puzzle optimization algorithm (FIPOA). Moreover, the high-frequency fusion is done by optimal weighted average fusion, and low-frequency fusion is carried out by filter mapping-based fusion. In the end, the inverse ONSST is taken to get the final integrated images. The experimental analysis of the recommended approach is evaluated with various performance measures. The validation sho

关键词： Multimodal image fusion image decomposition Low- and high-frequency fusion Optimal non-subsampled shearlet transform Fitness improved puzzle optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Demystifying the applications of wavelet Transform in Bio signal and Medical Data processing 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Antony, Linu Tess Kumar, S. N. APJAKTU Amal Jyothi Coll Engn Trivandrum Kerala India

ISBN: (数字)9789819713202

ISBN: (纸本)9789819713196;9789819713202

wavelet transforms have revolutionized bio signal and medical image processing by providing powerful tools for analysis and feature extraction. This article aims to demystify the applications of wavelet transforms in these domains, focusing on the DWT and WPT. These transforms allow for localized representation of signals and images in both time and frequency domains, enabling detailed analysis and relevant information extraction. In bio signal processing, wavelet transforms are used for denoising, feature extraction, and classification of ECG, EEG, and other bio signals. In medical image processing, wavelet transforms find applications in denoising, compression, image fusion, and segmentation, leading to improved analysis and diagnosis. The article provides an overview of these applications, highlighting advantages and presenting specific examples and results. wavelet transforms have great potential in bio signal and medical image processing, driving healthcare and biomedical research advancements.

关键词： wavelet transforms image processing signal processing Quaternion wavelet transforms Gabor wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Automatic dysarthria detection and severity level assessment using CWT-layered CNN model

引用

EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC processing 2024年第1期2024卷 1-14页

作者： Sajiha, Shaik Radha, Kodali Rao, Dhulipalla venkata Sneha, Nammi Gunnam, Suryanarayana Bavirisetti, Durga Prasad Velagapudi Ramakrishna Siddhartha Engn Coll Dept Elect & Commun Engn Vijayawada 520007 Andhra Pradesh India Univ Tennessee Hlth Sci Ctr Dept Pediat Div Pediat Neurol Memphis TN 38163 USA Norwegian Univ Sci & Technol Dept Comp Sci N-7034 Trondheim Norway

Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models' performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal's integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

关键词： Dysarthria Severity assessment TORGO UA-Speech CWT-layer Amor wavelet Morse wavelet Bump wavelet

来源：评论

学校读者我要写书评

暂无评论

Optical image watermarking based on orbital angular momentum holography

引用

Chinese Physics B 2024年第12期33卷 269-277页

作者： Jialong Zhu Jiaying Ji Le Wang Shengmei Zhao Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Information Engineering Suqian UniversitySuqian 223800China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

We propose an optical image watermarking scheme based on orbital angular momentum(OAM)*** topological charges(TCs,l)of OAM,as multiple cryptographic sub-keys,are embedded into the host image along with the watermark ***,the Arnold transformation is employed to further enhance the security and the scrambling time(m)is also served as another cryptographic *** watermark image is embedded into the host image by using the discrete wavelet transformation(DWT)and singular value decomposition(SvD)***,the interference image is utilized to further enhance *** imperceptibility of our proposed method is analyzed by using the peak signal-to-noise ratio(PSNR)and the histogram of the watermarked host *** demonstrate robustness,a series of attack tests,including Gaussian noise,Poisson noise,salt-and-pepper noise,JPEG compression,Gaussian lowpass filtering,cropping,and rotation,are *** experimental results show that our proposed method has advanced security,imperceptibility,and robustness,making it a promising option for optical image watermarking applications.

关键词： optical image watermarking orbital angular momentum holography discrete wavelet transformation

来源：评论

学校读者我要写书评

暂无评论

Improving time-frequency resolution in non-stationary signal analysis using a convolutional recurrent neural network

引用

signal image AND vIDEO processing 2024年第5期18卷 4797-4810页

作者： Krishna, B. Murali Satyanarayana, S. v. v. Satyanarayana, P. v. v. Suman, M. venkata Sri Vasavi Engn Coll ECE Dept Tadepalligudem 534101 Andhra Pradesh India

In this paper, a convolutional recurrent neural network (ConvRNN) wavelet-based kernel is proposed to improve the time-frequency localization of non-stationary signals. Time-frequency distributions (TFDs) are used to localize the power spectral density components of a signal simultaneously over the time-frequency plane. For various non-stationary signals (NSS) with different features, poor time-frequency resolution is always an inherent problem in all developed TFDs. This is mainly due to inefficient segmentation methods and improper kernel adaptation. In the current work, the fraction Bezier-Bernstein polynomial function is applied to model the NSS, and points of inflection are used for signal segmentation. From the obtained segments, statistical time-frequency features are extracted and fed to a ConvRNN for better time-frequency localization. The ConvRNN employs a convolution computation between input signal features and the proposed Newton-Raphson gradient algorithm (NRGA)-based wavelet function. The optimization of the ConvRNN network is achieved by incorporating a hybrid method that combines the principles of normalized adaptive gradient descent and momentum-based optimization, with an additional normalization step to enhance convergence and stability. The ConvRNN weights are updated in both forward and backward directions (resilient propagation) until a better correlation is achieved between signal segments and the wavelet kernel. It is observed that the proposed ConvRNN NRGA wavelet improves the time-frequency localization when compared with standard TFDs and state-of-the-art methodologies. Furthermore, the proposed ConvRNN model is compared with other CNN and RNN architectures for better practical interpretations.

关键词： Bernstein polynomial Segmentation wavelet Resolution Gradient

来源：评论

学校读者我要写书评

暂无评论

Strengthening wavelet based image steganography using Rubik's cube segmentation and secret image scrambling

引用

MULTIMEDIA TOOLS AND applications 2024年第33期83卷 78797-78825页

作者： Khandelwal, Jyoti Sharma, vijay Kumar Manipal Univ Jaipur Dept Comp & Commun Engn Jaipur India

image steganography is an extremely rich and significant exploration region that gives productive answers to some genuine and modern issues. This paper deals with secret image transmission and securing it from different attacks. The proposed image steganography technique uses a combination of image segmentation, pre-processing, and scrambling methos. The proposed technique begins with the segmentation of secret and cover images using Rubik's cube. The segmentation process breaks the images into six parts, then after they are stored in a segmented image dataset. In the second step, each segmented secret image is processed by the image pre-processing method. The proposed image pre-processing method helps the system select the appropriate cover image from the segmented cover image dataset, which makes the technique more robust. In the third step, before the embedding process, secret images are scrambled using the proposed scrambling method. Finally, the cover and secret images are processed by discrete wavelet transforms (DWT) and singular value decomposition (SvD) to generate the stego image. The proposed technique is tested on a variety of grayscale images, with peak signal-to-noise ratio (PSNR) values ranging from 32.27 to 30.77 (dB) and structural similarity index measure (SSIM) values ranging from 0.93 to 0.90 for different alpha values. The extracted secret image is analyzed using normalized correlation (NC) and naturalness image quality evaluator (NIQE) parameters. The NC values are above 0.99, and the NIQE values ranged from 3.3264 to 3.8468 for various extracted images. To analyze the proposed technique, measured value of entropy and an elapse time are 7.7956 and 6.489 s, respectively. Comparative studies are conducted in four main areas. The first comparative study tested the reliability of the proposed scrambling method by calculating the overall and diagonal correlation values of the secret image and scrambled secret image. The second comparative study test

关键词： NC NIQE Pre-processing Scrambling Segmentation wavelet

来源：评论

学校读者我要写书评

暂无评论

Seamless Optimization of wavelet Parameters for Denoising LFM Radar signals: An AI-Based Approach

引用

REMOTE SENSING 2024年第22期16卷

作者： Abdelfattah, Talaat Maher, Ali Youssef, Ahmed Driessen, Peter F. Mil Tech Coll Radar Dept Cairo 11588 Egypt Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada

Linear frequency modulation (LFM) signals are pivotal in radar systems, enabling high-resolution measurements and target detection. However, these signals are often degraded by noise, significantly impacting their processing and interpretation. Traditional denoising methods, including wavelet-based techniques, have been extensively used to address this issue, yet they often fall short in terms of optimizing performance due to fixed parameter settings. This paper introduces an innovative approach by combining wavelet denoising with long short-term memory (LSTM) networks specifically tailored for LFM signals in radar systems. By generating a dataset of LFM signals at various signal-to-noise Ratios (SNR) to ensure diversity, we systematically identified the optimal wavelet parameters for each noisy instance. These parameters served as training labels for the proposed LSTM-based architecture, which learned to predict the most effective denoising parameters for a given noisy LFM signal. Our findings reveal a significant enhancement in denoising performance, attributed to the optimized wavelet parameters derived from the LSTM predictions. This advancement not only demonstrates a superior denoising capability but also suggests a substantial improvement in radar signal processing, potentially leading to more accurate and reliable radar detections and measurements. The implications of this paper extend beyond modern radar applications, offering a framework for integrating deep learning techniques with traditional signal processing methods to optimize performance across various noise-dominated domains.

关键词： linear frequency modulation wavelet transform signal denoising long short-term memory deep learning in signal processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：