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wavelet applications in signal and image processing v

The proceedings contain 56 papers. The topics discussed include: review of recent results on optimal orthonormal subband coders;comparison of wavelet image coding schemes for seismic data compression;image quality measurement using the Haar wavelet;lossless image compression using wavelets over finite rings and related architectures;on consistent signal reconstruction from wavelet extrema representation;seismic imaging in wavelet domain: decomposition and compression of imaging operator;application of differential mapping and wavelet transform;usage of short wavelets for scalable audio coding;enhanced resolution control for video sequences;regularized multiresolution methods for astronomical image enhancement;weighted time-frequency and time-scale transforms for non-stationary signal detection;and a wavelet detector for distributed objects.

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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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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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A Novel Feature-Fusion-Based Sparse Masked Attention Network for Acoustic Echo Cancellation Using wavelet and STFT Synergies

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CIRCUITS SYSTEMS AND signal processing 2025年第4期44卷 2882-2901页

作者： Ishwarya, v. Soni Kothandaraman, Mohanaprasad VIT Univ Sch Elect Engn Chennai India

Deep learning-based acoustic echo cancellation (AEC) systems have advanced significantly, yet previous methods often rely on a single transform, such as short-time fourier transform (STFT) or constant Q transform, which limits feature richness and leads to heavy models. In contrast, this paper introduces a novel feature-fusion-based encoder-decoder with a sparse masked attention network, specifically designed to enhance echo and background noise suppression. Our model uniquely combines discrete wavelet transform (DWT) and STFT features, leveraging both transforms to achieve richer feature representation. By employing the Daubechies wavelet ("db4"), the model attains high time resolution for high frequencies and improved frequency resolution for low frequencies-crucial for effective noise cancellation. The STFT component captures temporal spectral content, complementing DWT's strengths. To handle double-talk scenarios, a sparse masked attention mechanism selectively focuses on relevant signal windows, reducing computational load while enhancing accuracy. This masked network enables a causal model suitable for real-time applications. Additionally, Smooth L1 loss promotes stable convergence during training. Experimental results on the AEC challenge dataset demonstrate that our model outperforms traditional methods, achieving superior echo return loss enhancement, perceptual evaluation of speech quality, and correlation coefficient, validating its effectiveness in robust echo cancellation and speech quality improvement.

关键词： Transformer Sparse masked attention wavelet transform Acoustic echo cancellation Deep learning

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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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A brain magnetic resonance image compression technique using wavelet-based SPIHT algorithm and capsule autoencoder

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Multimedia Tools and applications 2025年第13期84卷 11343-11358页

作者： Bindu, P.v. Jabeena, A. School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore India

Medical imaging plays a starring role in diagnosis and treatment. It provides clinically meaningful information and thus reduces uncertainty in diagnosis. E-services related to the health sector, like telehealth, telemedicine, etc., require the transmission and storage of these digital data to be cost-effective and reliable. This paper discusses a selective image compression model for brain magnetic resonance (MR) images to tackle the telehealth sector’s increasing bandwidth and storage requirements. The brain MR image undergoes a sequence of filtering operations as part of its preprocessing stage. Optimized Fuzzy C-Means clustering is utilized to segment the medically significant region of the pre-processed image from the background information. A wavelet transform-based SPIHT algorithm compresses the medically significant part, and a capsule autoencoder-based compression technique is used for the background data. The suggested algorithm has a higher compression ratio (28.66) and a higher Peak signal-To-Noise Ratio (PSNR) (43.6dB) than existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： image compression

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Editorial for the Special Issue "signal and image processing: From Theory to applications" (1st Edition)

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3705-3705页

作者： Seracini, Marco Univ Bologna Dept Phys & Astron Via Berti Pichat 6-2 I-40128 Bologna Italy

In an era defined by data-driven solutions, signal and image processing have emerged as cornerstones of technological innovation. From the theoretical frameworks that govern digital signal transformations to the practical applications reshaping industries, the field continues to evolve at an unprecedented pace. This Special Issue, titled “signal and image processing: From Theory to applications”, aims to bridge the gap between fundamental research and real-world implementation, highlighting the transformative impact of this ***, A.; Gerace, I.; Giorgetti, v. A Graduated Non-Convexity Technique for Dealing Large Point Spread Functions. Appl. Sci. 2023, 13, 5861. https://***/10.3390/***, X.; Tan, Z.; Zhao, N.; Wang, J.; Liu, Y.; Tang, Y.; He, P.; Li, W.; Sun, J.; Si, J.; et al. Suitable Integral Sampling for Bandpass-Sampling Time-Modulated Fourier Transform Spectroscopy. Appl. Sci. 2024, 14, 1009. https://***/10.3390/*** la vega, J.; Riba, J.-R.; Ortega-Redondo, J.A. Mathematical Modeling of Battery Degradation Based on Direct Measurements and signal processing Methods. Appl. Sci. 2023, 13, 4938. https://***/10.3390/***, H.; Tang, J.; Zhou, H. Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation. Appl. Sci. 2023, 13, 6943. https://***/10.3390/***, S.; Li, Y.; Li, H.; Wang, B.; Wu, Y.; Zhang, Z. visual image Dehazing Using Polarimetric Atmospheric Light Estimation. Appl. Sci. 2023, 13, 10909. https://***/10.3390/***-viala, C.; Correcher, A.; Blanes, C. Detection of Bad Stapled Nails in Wooden Packages. Appl. Sci. 2023, 13, 5644. https://***/10.3390/***, M.; Brown, S.R. Inpainting in Discrete Sobolev Spaces: Structural Information for Uncertainty Reduction. Appl. Sci. 2023, 13, 9405. https://***/10.3390/***, M.; vinti, G. Sampling by Difference as a Method of Applying the

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