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检索条件"任意字段=Conference on Wavelet Applications in Signal and Image Processing V"

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A Speech Enhancement Algorithm Combining wavelet Transform and Adaptive Filters 6

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Joorabchi, Marjan Panahi, Issa Firouzeh, Fereshteh Fakhar The University of Texas at Tyler Department of Electrical and Computer Engineering TX United States Sharif University of Technology School of Science and Engineering Iran The University of Texas at Dallas Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331506520

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with wavelet Transform significantly improves noise reduction, outperforming the use of wavelet Transform alone. © 2025 IEEE.

关键词： Adaptive filtering

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Multiresolution Encoder-Decoder Convolutional Neural Network for Magnetic Resonance image Segmentation

Multiresolution Encoder-Decoder Convolutional Neural Network...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Tarafdar, Kishore K. Meher, Aaditya Shah, Mirat Saifee, Qutubuddin Kumar, Dushyant Nimkar, Anant v. Jayasundar, Rama Gadre, vikram M. Department of Electrical Engineering Indian Institute of Technology Bombay India Department of Information Technology India Department of Computer Engineering India Sardar Patel Institute of Technology Mumbai India India Department of NMR All India Institute of Medical Sciences New Delhi India

ISBN: (纸本)9798350368741

In image segmentation by deep learning, encoder-decoder Convolutional Neural Network (CNN) architectures are fundamental for creating and learning representations. However, with many filters in these models, one often loses the tractability of these models and interprets them as black boxes. In this paper, we describe the design of a multiresolution encoder-decoder CNN having a distinct low-frequency processing encoder and mixed-frequency processing decoder. The encoder of the proposed architecture first creates a localized spatial-frequency representation of the feature space using a framelet system and then learns representations using trainable convolution layers while converging to segment structures. The decoder takes in the encoder output, including distinct low and high-frequency skip connections and reconstructs the Region of Interest (RoI) with accurate boundaries. Spatial attention layers for low frequencies and a wavelet-based self-attention for high frequencies in the decoder successively aid the reconstruction of the desired segment. The described network architecture is overall lossless by design, despite information loss in wavelet pooling layers because of the initial subband decomposition and separate low and high-frequency paths. The model is tested separately using T1-weighted brain Magnetic Resonance (MR) images with two classes and accurately segments large and small RoIs. The trained model also successfully segments MR volumes acquired by our group. © 2025 IEEE.

关键词： Magnetic Resonance Imaging multiresolution analysis semantic segmentation wavelet attention wavelet pooling

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Heart signal processing Using wavelet Analysis and Deep Learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

引用

International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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wavelet-based Filtering of Noisy images by Amplification of Low-Frequency Components in High-Frequency Subbands

Wavelet-based Filtering of Noisy Images by Amplification of ...

引用

Digital signal processing and its applications (DSPA), International conference on

作者： Y. S. Bekhtin v. T. Trinh Department of Automatics and Information Technologies in Control Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper describes a new method of noisy image restoration based on amplification of low-frequency components in high-frequency subbands, or details of fast wavelet transform (FWT). It makes a difference with respect to well-known wavelet filtering methods which are typically based on soft thresholding of wavelet coefficients belonging to high-frequency subbands of a noisy image. At the same time, the low-frequency component, or approximation can be used to extract the low-frequency components corresponding the details. It allows modifying the details of the FWT by adding the extracted low-frequency components that leads to increasing the signal-to-noise ratios in subbands. The paper contains the experimental results of modeling proving the effectiveness of the suggested method comparing to some widely used filtering methods, too. The results of modeling have shown that the proposed method allows improving quality of restored image by increasing both the signal-to-noise ratio and significantly reducing the number of ringing artifacts.

关键词： Wiener filters Filtering image edge detection Digital signal processing image restoration Noise measurement signal to noise ratio wavelet coefficients

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wavelet Analysis of Cardiograms in EDF Format

Wavelet Analysis of Cardiograms in EDF Format

引用

Digital signal processing and its applications (DSPA), International conference on

作者： Sinan v. Kurbanov Denis A. Andrikov Department of Mechanics and Control Processes Peoples' Friendship University of Russia (PFUR) Moscow Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The correlation of the wavelet shape and the characteristic ECG section has been confirmed, and wavelets for the identification of P-, R-, and T waves have been experimentally established. An algorithm for identifying R waves in an ECG has been developed and its operability has been verified. Using the algorithm developed, a graph of the dynamics of the heartbeat period is plotted as a function of time (period number). To identify the P and T waves, the wavelet image of the real ECG is constructed, and the presence of distortion of the shape of this image due to the influence of QRS complex and neighboring waves is established. Computer removal (filtering) of QRS complexes from the ECG is carried out, wavelet image of the remaining part is calculated. The possibility of determining the positions of the maximum P- and T-waves has been established from the wavelet image of the filtered ECG. The theoretical and practical value of this study consists in a new direction of wavelet analysis of cardiac signals, by successively applying several wavelets of a special form, to identify characteristic sections of the ECG in order to improve diagnostics. The proposed method also assumes intermediate filtering of the identified sections when moving from wavelet to wavelet. The development was carried out within the framework of computer generation of arrays of digital functions of the state of the heart, suitable for training artificial intelligence systems, and diagnostics using such systems. The practical results obtained can be applied in the development of a digital expert diagnostic system or a specific technical device.

关键词： Training Correlation Shape Heart beat Filtering Heuristic algorithms signal processing algorithms Electrocardiography wavelet analysis Distortion

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Identical Human Preference Alignment Paradigm for Text-to-image Models

Identical Human Preference Alignment Paradigm for Text-to-Im...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Sun, Haoyuan Xia, Bo Zhao, Yifei Chang, Yongzhe Wang, Xueqian Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798350368741

Implicit reward mechanism of Direct Preference Optimization (DPO) has facilitated its recent applications beyond large language models (LLMs), notably in aligning text-to-image models with human preferences. While promising results have been achieved with algorithms such as Diffusion-DPO, their reliance on the assumptions of the Bradley-Terry model could potentially lead to significant overfitting. In this paper, we propose the Step Identical Preference Alignment (SIPA) method, departing text-to-image alignment from the assumptions of Bradley-Terry preference model. We assess the performance of four models, Diffusion-DPO, SPO, and SIPA, alongside the original model, on the HPS-v2 test set, which focus on three key aspects: text-image alignment, human value alignment, and generation diversity. Experimental results show that SIPA matches or outperforms existing SOTA alignment methods, and even exceeds the original model in terms of generation diversity, which compellingly demonstrates SIPA's superiority in mitigating alignment overfitting. © 2025 IEEE.

关键词： human preference alignment preference modeling text-to-image model

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation 3

An Improved Digital Signal Processing for Signal Enhancement...

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3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Alam, Intekhab Baxi, Paramjit Sahoo, Barnali Garg, Mohan Roopashree, R. Thaj Mary Delsy, T. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Digital signal processing has become a fundamental tool in signal processing applications, such as speech and image processing, due to its ability to manipulate and examine signals in a digital form skillfully. A primary challenge in signal processing involves the presence of unwanted noise and interference, which can negatively impact the precision and quality of the processed signal. The proposed advanced DSP approach aims to improve signal enhancement and interference mitigation techniques using sophisticated algorithms and methods. The proposed state-of-the-art DSP approach includes noise reduction algorithms, such as adaptive filtering and spectral subtraction, to eliminate unwanted noise from the signal. Additionally, cutting-edge signal processing techniques, such as wavelet transforms and time-frequency analysis, are used for signal enhancement and demising. The proposed approach employs adaptive equalization techniques to mitigate interference from other signals and substantially improve the overall signal quality. The enhanced DSP approach is evaluated through simulation and real-world experiments, demonstrating its effectiveness in improving signal quality and reducing interference considerably. The proposed approach has the potential to significantly improve the accuracy and reliability of various signal processing applications considerably, making it a valuable tool in fields such as telecommunications, audio and video processing, and medical signal analysis. © 2025 IEEE.

关键词： image enhancement

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

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2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.v. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using vIvADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 IEEE.

关键词： image analysis

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Facial Deblurring and Recognition Using image processing and Machine Learning Techniques 8th

Facial Deblurring and Recognition Using Image Processing and...

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8th IFIP TC 12 International conference on Computer, Communication and signal processing, ICCCSP 2024

作者： Subrahmanyam, B.R. Janavi, T.S. Keerthana, v. Serajudeen, Ayesha Jumana Arunnagiri, A.M. Department of Electronics and Communication Engineering College of Engineering Guindy Anna University Chennai600025 India

ISBN: (纸本)9783031736162

The project addresses the challenge of accurately identifying blurred faces in computer vision and facial recognition. It introduces a novel framework that integrates deblurring techniques, utilizing point spread function deconvolution to enhance facial image quality. Principal Component Analysis (PCA) is employed for feature extraction, and a K-Nearest Neighbors (KNN) classifier is applied for face identification. The combined deblurring and PCA-transformed features improve matching and identification accuracy, particularly in scenarios with initially blurred images. Experimental validation on a real-world dataset demonstrates the efficacy of the proposed methodology. This approach not only enhances facial recognition accuracy but also lays the groundwork for future research in challenging practical applications, such as security and law enforcement. © IFIP International Federation for Information processing 2025.

关键词： Optical transfer function

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A Convolutional Recurrent Mixer Network For Radar Meteorological image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Pires, Rafael Gonçalves Santos, Daniel F.S. Calheiros, Roberto v. Papa, João Paulo Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan Department of Computing São Paulo State University Bauru Brazil Meteorological Research Institute São Paulo State University Bauru Brazil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. Korea Republic of Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798350368741

image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology. © 2025 IEEE.

关键词： Convolutional Neural Networks Deep Learning Meteorology Recurrent Mixer Network Super Resolution

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