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A New Design and VLSI Implementation of Symmetric Daubechies Wavelet Filter Bank for image processing applications

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

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Graph Filters for signal processing and Machine Learning on Graphs

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4745-4781页

作者： Isufi, Elvin Gama, Fernando Shuman, David, I Segarra, Santiago Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA Franklin W Olin Coll Engn Needham MA 02492 USA

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power;that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article provides a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations at the intersections of signal processing, machine learning, and application domains.

关键词： Filter banks Low-pass filters Information filters signal processing Filtering theory Convolution Machine learning Graph signal processing graph machine learning graph convolution filter identification graph filter banks and wavelets graph neural networks distributed processing collaborative filtering graph-based image processing mesh processing point clouds topology identification spectral clustering matrix completion graph Gaussian processes

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Performance Improvement for VOIP Based Systems

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期139卷 145-166页

作者： Singh, Sarvjit Panjab Univ ECE Dept UIET Chandigarh India

This study focuses on evaluating how well a wireless voice over internet protocol (VOIP) system works for 4G and 5G communications by using wavelets. It suggests creating a transmission system for wireless VOIP based on data from the wavelet transform (both image and audio data). Each wavelet's performance will be assessed using several metrics, including signal-to-noise ratio, peak signal-to-noise ratio, root mean squared error, percentage of retained signal energy, and compression ratio. Additionally, other factors such as packet loss, Delay, and Throughput will be analyzed. These parameters will be evaluated with both raw data (without wavelets) and data processed using different wavelets. The simulation results show that the proposed model performs better with various wavelets compared to Markov's model. Notably, the Daubechies wavelet family yields superior results compared to other types. This model could be beneficial for high-speed IP-based cellular systems where minimizing delay (latency) is crucial. For 5G systems, parallel computing techniques may be used to implement this model effectively.

关键词： IP based communication (4G/5G) wireless VOIP signal processing wavelets QOS (quality of service)

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Graph Wedgelets: Adaptive Data Compression on Graphs Based on Binary Wedge Partitioning Trees and Geometric wavelets

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2023年 9卷 24-34页

作者： Erb, Wolfgang Univ Padua Dipartimento Matemat Tullio Levi Civita I-35122 Padua Italy

We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings. We prove that continuous results on best $m$-term approximation with geometric wavelets can be transferred to the discrete graph setting and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way. Finally, we illustrate that this graph-based method can be applied for the compression of images as well.

关键词： image coding image segmentation Wavelet analysis Information processing Continuous wavelet transforms Topology Discrete wavelet transforms Binary graph partitioning big data compression geometric wavelets greedy algorithms on graphs non-linear approximation graph wedgelets

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Semi-Tensor Product Compressed Sensing With Its applications: A Review

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IEEE SENSORS JOURNAL 2025年第3期25卷 4096-4114页

作者： Zhou, Rongpei Li, Rongfa Wu, Yaqian Chen, Jie Hong, Jin Yu, Lisu Liu, Qiegen Zhang, Yudong Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Natl Univ Def Technol Coll Elect Engn Hefei 230027 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England

Recently, as an emerging signal processing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of image processing, communications, and bioinformatics. This article reviews the theoretical foundations, algorithmic designs, and practical applications of STP-CS. It begins by revisiting the basic concepts of compressed sensing (CS) and the definition of the semi-tensor product (STP), followed by a detailed discussion on the theoretical model of STP-CS, optimization of the measurement matrix, and reconstruction algorithms. Furthermore, the article explores the practical applications of STP-CS in areas such as sensor nodes, visual security, image encryption, and spectrum sensing, analyzing its performance advantages and potential challenges in these fields. A comprehensive analysis indicates that STP-CS offers significant benefits in saving storage space, reducing computational complexity, and enhancing data security, making it a promising technology in the field of signal processing.

关键词： Sensors Sparse matrices image reconstruction Size measurement signal processing Compressed sensing Optimization Data processing signal processing algorithms Reviews Compressed sensing (CS) image encryption semi-tensor product (STP) signal processing

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Innovative applications of Quantum signal processing in Network Security and image processing

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SPIN 2025年第2期15卷

作者： He, Jinfeng Qi, Zhengyang Zhang, Lin Wang, Zelin Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226000 Peoples R China

This thorough overview paper investigates how quantum computing could revolutionize communication systems by resolving the intrinsic drawbacks of classical signal processing. Due to the rapid improvements in technology, these systems are subject to increasing demands for larger connection densities and improved spectrum efficiency, which presents substantial hurdles for conventional signal processing. The acceptance and expansion of modern communication technologies are impeded by these obstacles, which are typified by a trade-off between computing complexity and communication performance. Based on quantum mechanics, quantum computing offers a powerful substitute for classical algorithms by introducing a new paradigm that drastically lowers computational complexity for certain applications. This paper explores the history of quantization in classical communication signals and explores the use of quantum computing techniques in critical applications such as data detection, multi-user detection and channel estimation. The work showcases sophisticated methods for image denoising and Fourier transformations, expanding the conversation to include quantum processing of image signals. These quantum methods show significant promise for improving spectral analysis efficiency, reducing noise and increasing processing speed and accuracy. This study offers a thorough basis for future research and technical innovation in the field of image and conventional signal processing by outlining the current state of the art and possible uses of quantum computing in the future.

关键词： Quantum networks wireless communication wireless signal processing

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Additive Gaussian noise removal based on generative adversarial network model and semi-soft thresholding approach

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MULTIMEDIA TOOLS AND applications 2023年第5期82卷 7757-7777页

作者： Khmag, Asem Univ Zawia Fac Engn Dept Comp Syst Engn Zawia Libya

In digital image analysis and processing field of study, noise reduction and suppression have been stated as a common query. However, it is mostly essential issue to demesne the fine edges and ridges and tiny texture while suppressing the noise in processing of the digital images. In order to avoid causing "Over-strangling" phenomenon, semi-soft thresholding model is exploited to classify the sharp edges of the contaminated images. In this study, a self-adjusting generative adversarial network GAN is utilized. This procedure is used to extract the fine edge of the noised digital images in order to improve the actual signal in the high frequency components where the main parts of the clean pixels may consider as noise pixels, and as a result delete the unwanted noise from the tested image that might cause over smoothing to the resulted images. In order to further denoise the contaminated digital image, adaptive learning GAN model throughout scoring machine is exploited. Therefore, it preserves the information of input image and feature maps, learns the correlation between global and local features, improves image restoration performance, and suppresses phenomena such as over-smoothing that tend to occur in wavelets-based denoising. The proposed method is an end-to-end network structure with CNN-based preprocessing methods. Experimental results demonstrate that, in comparison with state-of-the-art noise removal techniques, the proposed method has better visual quality, and the proposed method improves PSNR by 2.27 dB and 0.85 dB on average compared with state-of-the-art- denoising methods. In addition, the proposed method could shorten the processing time noticeably.

关键词： Generative adversarial networks image denoising image enhancement Thresholding optimization Second-generation wavelets

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Application of Continuous Wavelet Transform for Time-of-Flight Ranging applications Using air-Coupled pMUTs

Application of Continuous Wavelet Transform for Time-of-Flig...

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2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium

作者： Sarafianou, Mantalena Choong, David Sze Wai Goh, Duan Jian Merugu, Srinivas Zhang, Qing xin Chang, Peter H. K. Giusti, Domenico Leotti, Alberto Scaldaferri, Rossana Kunnavakkam, Madan Koh, Yul ASTAR Inst Microelect IME 2 Fusionopolis WayInnovis 10-20 Singapore 138634 Singapore STMicroelectronics Catania Italy STMicroelectronics Singapore Singapore

ISBN: (纸本)9798350371918;9798350371901

This work presents, for the first time, the use of Continuous Wavelet Transform (CWT) for ultrasonic ranging with air-coupled pMUTs. wavelets is a powerful mathematical tool which to date has been used towards audio analysis and image processing. Given that wavelets are characterized by two properties, namely scale and time, they could be suitable for pMUT-based time-of-flight (ToF) ranging. The proposed signal processing methodology is experimentally validated with the method achieving a impressive mean accuracy of 95.5%. These experimental results highlight the potential usage of the proposed signal processing methodology for pMUT-based airborne ranging applications.

关键词： pMUT wavelets continuous wavelet transform ultrasonic time-of-flight estimation airborne ranging application

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Discrete fourier analysis and wavelets : applications to signal and image processing / Second edi...

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2018年

作者： Broughton S. Allen

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Face image Authentication Scheme Based on Cohen-Daubechies-Feauveau wavelets 1

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12th International Conference on Robotics Vision signal processing and Power applications

作者： Al-Hadaad, Muntadher H. Thabit, Rasha Zidan, Khamis A. Khoo, Bee Ee Al Iraqia Univ Coll Engn Comp Engn Dept Baghdad Iraq Dijlah Univ Coll Comp Tech Engn Baghdad Iraq Al Iraqia Univ Al Iraqia Univ Sci Affairs Baghdad Iraq Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Penang Malaysia

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

The recent interest of face image manipulation detection has been directed towards providing the ability of detecting various types of manipulations. In the best scenario, the available methods can detect the manipulations and localize the manipulated face region. The ability of recovering the face region after manipulation localization will be very useful in practical applications, however, this has not been highlighted in the previous researches. In this paper, a new face image authentication (FIA) scheme is presented based on image watermarking and Cohen-Daubechies-Feauveau (CDF) wavelets. In the proposed scheme, the CDF is used to generate the recovery bits from the face region in order to be used for recovering the face region when manipulations exist. Several experiments have been conducted to evaluate the performance of the proposed scheme which proved its efficiency in generating high quality watermarked images, detecting various types of manipulations, localizing the manipulated blocks in the face region, and recovering the face region with good visual quality. The comparison with the state-of-the-art detection schemes proved the superiority of the proposed scheme.

关键词： DeepFakes reveal Face image manipulation detection Face image authentication Multimedia forensics

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