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wavelet applications in signal and image processing vi

The proceedings contains 24 papers from the Conference on wavelet applications in signal and image processing vi. Topics discussed include: comparison of wavelets from the point of view of their approximation error;least statistically dependent basis and its application to image modeling;bivariate box spline wavelets in Sobolev spaces;pseudoframes for subspaces with applications;multiscale B-spline snakes for general contour detection;multilayered image compression;warping functions in speech;and fast algorithm for directional time-scale analysis using wavelets.

关键词： Optical filters

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Erratum: (wavelet applications in signal and image processing vi (22-23 July 1998))

Proceedings of SPIE - The International Society for Optical ...

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Proceedings of SPIE - The International Society for Optical Engineering 1998年 3458卷 275-275页

作者： Laine, Andrew F. Unser, Michael A. Aldrvoubi, Akram

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

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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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Parametric Monogenic Linear Canonical wavelet Transform

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CIRCUITS SYSTEMS AND signal processing 2025年 1-22页

作者： Chen, Jian-Yi Li, Bing-Zhao Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab MCAACI Beijing 100081 Peoples R China

This paper begins by examining the definition and fundamental properties of the two-dimensional linear canonical wavelet transform (2-D LCWT) within the framework of linear canonical transform theory. The LCWT offers significant advantages in handling multi-scale and multi-directional signals. Building on this, a novel model is proposed based on the parametric Riesz transform and parametric monogenic signal, introducing a parametric monogenic linear canonical wavelet along with its associated transform, referred to as PMLW. Through parametric embedding within the monogenic linear canonical wavelet framework, the proposed transform achieves enhanced flexibility and robustness, facilitating more efficient analysis of intricate features in complex signals. This approach leverages 2-D analytical signal theory to incorporate phase information with directional characteristics, thereby preserving rich directional details in multi-scale analysis. Furthermore, the potential applications of PMLW in image denoising tasks are explored, demonstrating its effectiveness in preserving structural details while suppressing noise.

关键词： Linear canonical transform Analytic wavelet transform Riesz transform Monogenic signal Analytic signal

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Simplified and Efficient 2-D DWT Hardware Design for visual Sensor

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IEEE SENSORS JOURNAL 2025年第6期25卷 9738-9746页

作者： Dai, Yuzhou Zhang, Wei Li, Qitao Wu, Zhuolun Liu, Yanyan Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Nankai Univ Coll Elect Informat & Opt Engn Tianjin 300071 Peoples R China

In this article, a simplified efficient 2-D discrete wavelet transform (DWT) architecture based on the lifting scheme is presented. By approximating the multipliers required for the multiplication operations in the Cohen-Daubechies-Feauveau (CDF) 9/7 filter, computational resources are significantly reduced. In addition, the strip-based scanning method requires minimal storage resources;for single-level 2-D DWT, there is no need for RAM resources that are dependent on image size. This characteristic makes the proposed architecture particularly well-suited for applications in wireless visual sensor networks (WVSNs). The proposed 2-D DWT and IDWT hardware implementations ensure a reconstructed quality exceeding 90.56-dB peak signal-to-noise ratio (PSNR) for various N x N images with low power consumption. Compared to existing 2-D DWT architectures, our design offers significant advantages, particularly when processing larger block sizes. The area-delay product (ADP) is reduced by at least 10.02%, the energy per image (EPI) decreases by 75.98%, and power consumption is lowered by 53.52%. Furthermore, the circuit performance in the multilevel architecture is outstanding, and the savings in required resources and energy make the proposed architecture highly suitable for applications in WVSNs.

关键词： Discrete wavelet transforms Adders Computer architecture Sensors Hardware Wireless sensor networks visualization Parallel processing Wireless communication Strips 2-D discrete wavelet transform (DWT) hardware design lifting architecture very-large-scale integration (VLSI) visual sensor

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The effect of wavelet transform on the classification performance of different deep learning architectures

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signal image AND viDEO processing 2025年第5期19卷 1-15页

作者： Kelek, Muhammed Mustafa Fidan, Ugur Oguz, Yueksel Afyon Kocatepe Univ Fac Technol TR-03000 Afyonkarahisar Turkiye Afyon Kocatepe Univ Fac Engn TR-03000 Afyonkarahisar Turkiye

This study focuses on classifying nine different types of fruit using several deep learning architectures, including AlexNet, ResNet-50, GoogleNet, DenseNet-201, and EfficientNet-b0, while also investigating the effect of wavelet Transform (WT) on the classification success rates of these models. The research begins by testing these architectures on a clean, well-curated dataset, where AlexNet, ResNet, GoogleNet, DenseNet, and EfficientNet achieve impressive success rates of 93.88%, 100%, 99.34%, 99.94%, and 99.88%, respectively. However, when the models are tested on an online dataset of fruit images sourced from the internet to reflect better real-life conditions, the success rates drop significantly to 63.10%, 76.35%,74.64%, 76.63%, and 73.07%, respectively. This drop highlights the challenges posed by real-world noise, varying image backgrounds, and inconsistencies in data quality. To address this issue, the study incorporates WT as a preprocessing step and finds that it significantly improves classification accuracy, particularly for the GoogleNet architecture. The results emphasize the importance of considering real-world complexities such as background noise and image distortion when developing classification models. Furthermore, the study demonstrates the potential of WT to increase classification performance, especially in specific architectures such as GoogleNet, which shows the most significant improvement. In addition, the effect of WT was statistically analyzed and the results were evaluated. Overall, this research contributes to a deeper understanding of the factors that affect the performance of deep learning models in fruit classification tasks and provides valuable insights into optimizing these models for more robust real-world applications.

关键词： Artificial intelligence Deep learning wavelet transform AlexNet GoogleNet ResNet-50 DenseNet-201 EfficientNet-b0

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An adaptive ACM watermarking technique based on combined feature extraction and non-linear equation

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EXPERT SYSTEMS WITH applications 2025年 275卷

作者： Bounceur, Ahcene Kara, Mostefa Ferik, Brahim Laouid, Abdelkader Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Acad Belt Rd Dhahran 31261 Saudi Arabia Univ Tebessa Lab Math Informat & Syst LAMIS Tebessa 12002 Algeria Univ Oued LIAP Lab POB 789 El Oued 39000 Algeria

Digital watermarking plays a vital role in safeguarding data integrity and intellectual property, particularly in medical imaging, where ensuring authenticity and preserving privacy are paramount. Embedding hidden information within images aids authentication, copyright protection, and integrity verification. This work proposes a robust palmprint-based watermarking approach based on adaptive ACM watermarking with anon- linear equation technique to enhance patient security. The proposed method integrates local binary patterns and histograms of oriented gradients for robust feature extraction, discrete wavelet transforms for multilevel image decomposition, Arnold Cat Map for chaotic mapping, and singular value decomposition for stable watermark embedding. The contribution goals are improving security, imperceptibility, and resilience against image processing attacks. Experimental results demonstrate the method's effectiveness, with a peak signal-tonoise ratio of 63.52 and a structural similarity index of 1.00, indicating high image quality retention. An equal error rate of 0.035 confirms the method's reliability in watermark detection. These findings underscore the proposed method's suitability for secure medical image watermarking applications, enhancing data integrity and patient privacy.

关键词： Medical image Watermarking Palmprint features Integrity Discrete wavelet Transform Singular Value Decomposition

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An adaptive approach for securing patient data in intellectual disability care with 8D Hyperchaotic DNA encryption and IWT

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BIOMEDICAL signal processing AND CONTROL 2025年 100卷

作者： Anisha, J. R. Teen, Y. P. Arul Univ Coll Engn Nagercoil Tamil Nadu India

Intellectual disability (ID) is a neurodevelopmental disease characterized by significant intellectual and adaptive functioning impairments. Continuous monitoring and data collection are crucial for managing this condition. To collect patient's skin temperature data, doctors and caretakers often use thermal IR imaging, and they must securely share this information. To address privacy concerns and ensure robust encryption during data transmission, this study introduces an adaptive encryption algorithm that operates in both frequency and spatial domains. The algorithm counteracts various security threats, including plain text attacks, differential attacks, brute force attacks, and cropping attacks. The core of this research is the 8D Hyperchaotic DNA Encryption Algorithm, which enhances security by combining block scrambling with DNA coding. Security is further optimized through integrating the Integer wavelet Transform (IWT) in the frequency domain and a DNA sequence in the spatial domain. The encryption process aims to balance security and computational efficiency, ensuring reliable performance. Extensive testing and validation against multiple types of attacks show the algorithm's effectiveness and reliability in practical applications. Experimental results show that the proposed system significantly enhances security against various attacks, thus setting a higher standard for encryption in biomedical image and signal processing. This algorithm shows potential for broader applications beyond intellectual disability care, including secure data transmission in medical imaging, financial transactions, and confidential communications, because of its robust encryption capabilities and adaptability to different data types.

关键词： Sensitive patient data Encryption algorithm 8D Hyperchaotic DNA Encryption Data security Data privacy Integer wavelet transform (IWT) DNA coding

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Blind Medical image Watermarking Based on LBP-DWT for Telemedicine applications

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CIRCUITS SYSTEMS AND signal processing 2025年 1-26页

作者： Hebbache, Khaled Aiadi, Oussama Khaldi, Belal Benziane, Ali Kasdi Merbah Univ Comp Sci Dept Ghardaia RdBP511 Ouargla 30000 Algeria Lab Intelligence Artificielle & Technol Informat L Ouargla 30000 Algeria Univ Ziane Achour Fac Sci & Technol Djelfa Algeria

With the increasing use of telemedicine in healthcare, the security and integrity of medical images during transmission have become critical. This paper presents a novel blind watermarking scheme Local Binary Pattern-Discrete wavelet Transform (LBP-DWT) for medical images based on Local Binary Patterns and the *** Transform in frequency domain. We take advantage of the LBP, which is computationally fast, to improve the watermark's resistance to the different kinds of attacks, and maintain the overall visual quality of the watermarked images. In addition, the DWT could offer a high trade-off between robustness and imperceptibility due to the multi-resolution analysis it provides. During embedding, the LL band (approximation coefficients) of the DWT is selected and divided into 3 x 3 blocks. The resulting LBP codes are then XORed with the embedding bits and hidden in the corresponding blocks using the Least Significant Bit technique. Note that the Arnold transform is used during the embedding step to scramble the watermark, which is then vectorized based on the ZigZag fashion to improve the security of the proposed scheme. To evaluate the performance of the proposed method, extensive experiments are conducted on a dataset of medical images. The watermarked images are tested against various attacks, including compression, noise addition, and cropping. The obtained results demonstrate the effectiveness of the proposed techniques.

关键词： Watermarking Blind Telemedicine Discrete wavelet transform Local binary patterns

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wavelet-integrated deep neural network for deblurring and segmentation of crack images

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MECHANICAL SYSTEMS AND signal processing 2025年 224卷

作者： Sun, Rui Li, Xuming Zhang, Libing Su, Yi Di, Jin Liu, Gang Chongqing Univ Sch Civil Engn Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China Yunnan Key Lab Water Resources & Hydropower Engn S Kunming 650032 Peoples R China

The blurred concrete crack images are typically the result of unexpected camera motion in engineering. Consequently, deblurring and segmentation of them represents a challenging task due to the complexity of the issue. This paper presents a novel Deep Multi-stage Network (DMNet), integrated with a multi-stage feature fusion strategy, for the purpose of deblurring crack images. The proposed model is compared to other models and found to be superior. Furthermore, a novel Discrete wavelet Transform Network (DWTNet) which integrated with a multi-stage fusion approach is proposed to improve the semantic segmentation results of restored crack images. Two crack image datasets are employed to validate the proposed models and represent the typical blurring scenarios in engineering applications. The SSIM, Dice and IoU metrics are employed to provide a quantitative assessment of the proposed DMNet and DWTNet models' capabilities in terms of image deblurring and segmentation. The values of these metrics exceed those of existing models, thereby demonstrating that the DMNet combined with the DWTNet is more effective than other state-of-the-art models. It exhibits superior performance in deblurring and segmentation of crack images, respectively.

关键词： Crack images Deblurring Segmentation DMNet DWTNet

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