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signal Restoration with Fractional Transform Sparse Representation and Autoencoder 9

Signal Restoration with Fractional Transform Sparse Represen...

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9th International conference on Computer and Communication Engineering, ICCCE 2023

作者： Saxena, Shivani Mistry, Surbhi Patel, Nilesh Roy, Mohendra Rizvi, Ahsan Z. Institute of Advanced Research Computer Sciences and Engineering Gandhinagar India Pandit Deendayal Energy University Information and Communication Technology Gandhinagar India

ISBN: (纸本)9798350325218

Signa1 denoising is a crucial task in image processing, audio analysis, and communications. Traditional methods often struggle to strike a balance between reducing noise and preserving important signal details. Existing methods exploit the potential of fractional transforms combined with sparse representation techniques. However, these approaches face challenges in achieving the desired balance due to limited sensitivity to signal variations, high computational complexity, restricted adaptability to diverse noise types, and the need for careful selection of transform parameters. To overcome these limitations, we propose a novel method called Fractional Transform Sparse Representation Denoising (FTSRD), which integrates fractional wavelet transform (FWT), fractional DCT (FDCT), sparse representation, and Deep Denoising Autoencoder (DDAE) techniques. The integration of DDAE in FTSRD provides several advantages. DDAE, being a deep learning-based model, has the capability to learn complex noise patterns and extract meaningful signal features. Experimental evaluations were conducted on synthetic signals and ECG signals. The results demonstrate the superior performance of FTSRD in reducing noise while preserving essential structural details. It also surpasses traditional methods in subjective and objective quality measures. In conclusion, FTSRD addresses the challenges associated with noise reduction and signal detail preservation, making it a valuable tool in various denoising applications. © 2023 IEEE.

关键词： signal reconstruction

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Comparison of Widely-used image Restoration Methods for Additive and Multiplicative Noises

Comparison of Widely-used Image Restoration Methods for Addi...

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

作者： Mohammad Zedan Yury S. Bekhtin dept. Diagnostic Information Technologies National Research University “MPEI” Moscow Russian Federation dept. Automatics and Information Technologies in Control Ryazan State Radio Engieneering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This paper evaluates and compares the performance of various image denoising techniques, including wavelet transform, Wiener filtering, non-local means (NLM), denoising autoencoders (DAEs), and feedforward autoencoders. There are cases of additive and multiplicative noises considered where the Gaussian distribution with zero mean stands for the first case and an exponential distribution with unity mean describes the second case (speckle). The experiments have been conducted on the given dataset which contained both still test images and synthetic aperture radar (SAR) images with varying noise levels to analyze the effectiveness of these denoising approaches. The performance of each method is assessed using key image quality metrics: Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Normalized Absolute Error (NAK). The results demonstrate that denoising autoencoders DAEs provide superior performance in terms of structural preservation and noise reduction, particularly in low-noise conditions, while Wiener filtering proves to be computationally efficient and robust under high-noise conditions. These findings offer insights into the selection of effective denoising techniques for noisy images based on specific noise characteristics and application requirements.

关键词： wavelet transforms Wiener filters Additives PSNR Autoencoders Speckle Radar polarimetry Computational efficiency image restoration Noise level

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Radar signal Recognition Based on MSST and Dual Channel Feature Extraction 18

Radar Signal Recognition Based on MSST and Dual Channel Feat...

引用

18th IEEE conference on Industrial Electronics and applications, ICIEA 2023

作者： Shi, Yonglin Quan, Daying Zhang, Zheng Tang, Zeyu Sun, Jia China Jiliang University Hangzhou China Jptek Corporation Limited Hangzhou Hangzhou China

ISBN: (纸本)9798350312201

Aiming at the problems of the traditional low probability of intercept (LPI) radar signal recognition algorithm, which is difficult to extract effective features and low recognition accuracy under low signal-to-noise ratio (SNR), a radar signal recognition method based on multi-synchrosqueezing transform (MSST) and two channel feature extraction was proposed. First, MSST is used to obtain signal time frequency images, and perform a series of pre-processing operations. Then, the image features are extracted through the gray co-incidence matrix (GLCM) and the local binary variance mode (LBPV), and the feature dimension is reduced by the principal component analysis (PCA). Finally, the dimensionality reduction features are sent to the support vector machine (SVM) to complete the classification and recognition of radar signals. According to the simulation analysis results, the recognition accuracy of the two-channel feature extraction algorithm is 15.67% higher than that of the two single channels on average under a low SNR and significantly higher than that of the symmetric Holder coefficient method. Moreover, when the SNR is as low as -2dB, the overall recognition rate of the algorithm is still 96.44%. This algorithm not only has low computational complexity but also has high recognition accuracy, especially compared with single-channel feature extraction and symmetric Holder coefficient methods at low SNR, the recognition accuracy has significant advantages. © 2023 IEEE.

关键词： Feature extraction

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Domain-Specific Adaptation for Enhanced Gait Recognition in Practical Scenarios 24

Domain-Specific Adaptation for Enhanced Gait Recognition in ...

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6th International conference on image, video and signal processing, IVSP 2024

作者： Jaiswal, Nitish Huan, vi Duc Limanta, Felix Shinoda, Koichi Wakasa, Masahiro Asilla Inc. Japan Asilla Inc. Viet Nam Dept. Of Computer Science Tokyo Institute Of Technology Japan

ISBN: (纸本)9798400716829

Gait recognition is a burgeoning field within biometric recognition which utilizes computer vision technology to extract silhouette images or body skeletons to identify users by leveraging the unique walking patterns of individuals. However, with its huge potential for user identification in diverse settings especially in security and surveillance applications, it faces challenges in transitioning from controlled datasets to real-world applications. In the regime of silhouette-based models, the most challenging covariate is associated with varying viewing angles, which has often been a bottleneck to achieving optimal accuracy for practical application in a real-world situation. Addressing this challenge, this paper introduces a novel domain adaptation technique tailored for gait recognition for practical applications, capitalising on expansive dataset pretraining and precise fine-tuning on targeted, smaller datasets pertaining to specific camera views. Our deep dive reveals that models adopting this adaptive training approach, especially when fine-tuned with viewing angles mirroring the test domain, witness a significant boost in pure cross-domain performance. Moreover, in a stride towards practical gait recognition, we present Asilla-Office - a non-synthetic dataset captured in an indoor office simulating real walking patterns of people in a real application environment. With its roots in real-world challenges, Asilla-Office is poised to be an initial benchmark, promoting research reflecting genuine application needs. In-depth experiments show that our domain-adapted fine-tuning approach trumps traditional single-staged training, marking a notable leap of more than 11% in Rank-1 accuracy on the new Asilla-Office dataset. In the spirit of fostering community-driven progress, the Asilla-Office dataset will be made publicly available. © 2024 ACM.

关键词： Benchmarking

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A High-Speed Computational Pipeline Single MAC-Based VLSI Architecture for Real-Time signal and image processing 4

A High-Speed Computational Pipeline Single MAC-Based VLSI Ar...

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4th International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Vaithiyanathan, Dhandapani James, Britto Pari Mariammal, Karuthapandian National Institute of Technology Delhi Delhi110036 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Chennai600062 India Anna University Madras Institute of Technology Chennai600044 India

ISBN: (纸本)9798350343670

VLSI architectures play a vital role in most real-time applications because of their optimal performance. In this work, a single MAC (Multiply-Accumulate) based pipelined architecture is proposed for faster computation of the one-dimension (1-D) discrete wavelet transform (DWT). The main objective of this work is to reduce computational complexity by utilizing resource sharing and to enhance the speed of operation by employing a pipelining scheme. The designed single MAC-based architecture for computing DWT is suitable for infrasound data processing which employs a high-pass filter (HPF) and low-pass filter (LPF). The architecture efficiently implements a resource-sharing scheme by adopting only these two filters. In addition, in this realization, pipelining is utilized to achieve high-speed computation of DWT. Further, this design makes use of common elements several times which provide a convenient and suitable implementation. The proposed structure is coded using Verilog and implemented in FPGA (Field Programmable Gate Array) hardware with the Xilinx platform. Intending easily transported and real-time utilization, this architecture is realized using Xilinx virtex-5 series FPGA. The area of the proposed architecture is reduced by about 44.8% when examining conventional parallel MAC architecture. Likewise, this pipelined design offers power conservation of about 31.97% over the parallel design. Further, the proposed architecture offers an operating speed of 276MHz with a power conservation of 200mW at 276MHz clock frequency. © 2024 IEEE.

关键词： DWT FPGA Device High-Speed Low-Power MAC Pipelining VLSI Architecture

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Automatic Diagnosis of Type of Glaucoma Using Order-One 2D-FBSE-EWT 24

Automatic Diagnosis of Type of Glaucoma Using Order-One 2D-F...

引用

24th International conference on Digital signal processing and its applications, DSPA 2022

作者： Chaudhary, Pradeep Kumar Jain, Sujay Damani, Tina Gokharu, Shirali Pachori, Ram Bilas Indian Institute of Technology Indore Department of Electrical Engineering Indore453552 India Choithram Netralaya Department of Ophthalmology Indore453001 India

ISBN: (纸本)9781665494434

This paper presents a framework for the automatic classification of Primary Angle-Closure Glaucoma (PACG), Pri-mary Open-Angle Glaucoma (POAG), and secondary Glaucoma from a normal subject. Order-one two-dimensional-Fourier-Bessel series expansion-empirical wavelet transform (2D-FBSE-EWT) based fusion ensemble ResNet-50 model is used in this work. Order-one 2D-FBSE-EWT decomposes the fundus images into sub-images. Subsequently, each sub-image is fed to the ResNet-50 model for extraction of deep features. Thereafter, deep features from each sub-images are ensembled. The ensembled features are then reduced using principal component analysis, and finally the reduced features are fed to a Softmax classifier for classification. Besides this approach, 4-channel, 3-channel (diagonal-wise grouping), and 2-channel (diagonal-wise grouping and neglecting diagonal detail component) sub-image groupings are also compared at 5-fold and 10-fold cross-validation. The 3-channel order-one 2D-FBSE-EWT based fusion ensemble ResNet-50 model provided an accuracy of 93% for the balanced database whereas it was limited to an accuracy of 78.3% for the unbalanced database at 10-fold cross-validation. © 2022 IEEE.

关键词： wavelet transforms

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Adaptive wavelet Techniques for Pattren Analysis

Adaptive Wavelet Techniques for Pattren Analysis

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the conference on

作者： Munira H. Abdallatif Ahmed E. Rgibi Amany K. Alarbish Sana A. Abdaljlil Abdallah A. Oshah Faculty of Engineering Sabratha University Sabratha Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

wavelet analysis, a computational method, decomposes data into frequency components, analyzed by scale, effective for signals with discontinuities. Originating from diverse fields like mathematics, physics, and engineering, wavelet theory finds applications in image compression, radar, and more. Using scalable wavelet functions, it allows precise signal reconstruction, differing from constant scale methods. This study compares adapted and traditional wavelets, emphasizing Finite Impulse Response (FIR) efficiency. Applying these wavelets for pattern recognition, particularly in spike detection in EEG data, highlights their versatility in complex signal processing.

关键词： Wireless communication Filters Tensors Finite impulse response filters Radar imaging wavelet analysis signal reconstruction

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High dimensional Bio medical images denoising using wavelet transform and modified bilateral filter 14

High dimensional Bio medical images denoising using wavelet ...

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14th International conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Jabbar, Muhammad Usama Waqar, Ali Awais, Muhammad Mushtaq, Zohaib Abbass, Muhammad Jamshed Rehman, Muhammad Abdul Saleem, Faisal Ahmed, Shehzad Jan, Ahmed Zubair Natl Univ Sci & Technol Dept Elect Engn Islamabad Pakistan Riphah Int Univ Fac Engn & Appl Sci Islamabad Pakistan Univ Napoli Parthenope Naples Italy Wroclaw Univ Sci & Technol Fac Mech Engn Wroclaw Poland

ISBN: (纸本)9781665460712

Biomedical images consist of salient information that helps in diagnosis of diseases. Significant amount of information is lost during process of transmission due to addition of noisy components especially for Computed Tomography (CT) scan, Ultrasound imaging and Magnetic Resonance Imaging (MRI). image denoising is an important and necessary pre-processing phase in image applications. Numerous techniques have been developed for denoising process over decades. The purpose is to remove noise from images. wavelet transform is current state of the art addition to analyze image denoising;able to achieve improved experimental results than from existing techniques. Generalized Gaussian distribution is considered to model noise. In this article, we introduce mix adaptive thresholding with modified bilateral filter. Simulation results demonstrate that attributes of images are improved. The Qualitative and quantitative assessment proves better denoised results for proposed method using parameters like Peak signal to Noise Ratio (PSNR), Mean Opinion Score (MOS) and Structural Similarity Index (SSIM) between the true and estimated biomedical images.

关键词： Gaussian noise wavelet thresholding biomedical images denoising PSN SSIM

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Research on Anomaly Test Data processing Method Based on wavelet Analysis

Research on Anomaly Test Data Processing Method Based on Wav...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Kong Xiangqi Wang Zhongyuan Unit 41 of 92941 Troops PLA Huludao China

A large amount of test data and test data will be generated in the stages of equipment development, design, setting and use. Meanwhile, due to the easy impact of external environment in the process of data collection, the quality of collected data will be reduced. If the processing of abnormal data is not accurate enough, the analysis and evaluation of test results will be affected. Study the corresponding data processing methods to ensure the accuracy of test data and support the analysis and evaluation of test results. Aiming at the problems existing in traditional abnormal data processing methods, this paper optimizes the existing wavelet analysis based on theoretical research and mathematical calculation, and summarizes an effective method for processing signal abnormal data, which provides effective support for the analysis and interpretation of test results.

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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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