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

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Blind Digital Watermarking Method Based on Quadtree Decomposition and Complex wavelet Transform for Medical Application

Blind Digital Watermarking Method Based on Quadtree Decompos...

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2022 IEEE International conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Nouioua, Nadhir Seddiki, Ali Ghaz, Abdelkrim Djillali Liabes University Telecommunications and Digital Signal Processing Laboratory Sidi Bel-Abbes Algeria Djillali Liabes University Communications Networks Architecture and Multimedia Laboratory Sidi Bel-Abbes Algeria

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

A digital watermarking scheme based on dual-tree complex wavelet transform (DTCWT), and the quadtree (QR) is suggested in this paper, in this technique the DTCWT is exerted on the host image as a first step in the process, then decomposing the low frequencies using the quadtree which illustrates the data structure, to obtain a suitable position for the watermark insertion, once the decomposition is done, the watermark is incorporated in the consequent block of the quadtree using a blind embedding function based on quantization. At the extraction phase, the watermarked image is treated similarly to the embedding phase, then an extraction formula based is applied to retrieve the watermark. The experiments had shown the superior results achieved in terms of high fidelity and similitude, furthermore, a comparison was effectuated as well as to prove the suggested technique's effectiveness. © 2022 IEEE.

关键词： Medical applications

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Fluorescence image Denoising Based on Self-supervised Deep Learning 7

Fluorescence Image Denoising Based on Self-supervised Deep L...

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7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Huang, Haojian Liu, Yixuan Li, Yang College of Computer Science and Technology Harbin Engineering University Harbin Heilongjiang150001 China College of Transportation Engineering Tongji University Shanghai201804 China College of Electrical Information Wuhan Institute of Technology Wuhan430200 China

ISBN: (纸本)9781665478571

Fluorescence microscopy imaging technology is a crucial imaging technology widely used in biomedical fields such as brain science. However, its images often have random noise without a fixed pattern, causing a series of problems such as image degradation, image segmentation, and classification misalignment. In order to obtain higher signal-to-noise ratio imaging, the input of higher doses of photons increases the cost and causes adverse effects such as photobleaching, phototoxicity, or tissue heating. Therefore, researchers have studied many traditional denoising methods and deep learning-based denoising methods. Traditional denoising methods have the advantage of fast and straightforward processing and tend to perform well when the noise level of the image is low, or the noise distribution is known. However, its effectiveness and generalization are minimal when the noise level is high and randomly distributed without a fixed modality. On the contrary, image denoising methods based on self-supervised deep learning can extract high-dimensional features of images, have better effectiveness and robustness in practical denoising applications. Morever, these methods reduce the need for manual annotation of large-scale data, making timely research on cutting- edge pathology possible when there are insufficient labeled data. In this paper, to tackle the denoising problem of fluorescence microscopy images, two classic traditional denoising methods, Wiener filtering, and wavelet transformation, and methods based on deep learning, DeepCAD, are used for comparative experiments. The experimental results show that the deep learning method has good effectiveness and generalization in dealing with high-level random noise, and it performs better than traditional denoising methods. © 2022 IEEE.

关键词： wavelet transforms

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation

An Improved Digital Signal Processing for Signal Enhancement...

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Automation and Computation (AUTOCOM), International conference on

作者： Intekhab Alam Paramjit Baxi Barnali Sahoo Mohan Garg 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 Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

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.

关键词： Wireless communication wavelet transforms Time-frequency analysis Accuracy Prevention and mitigation signal processing algorithms Interference Digital signal processing wavelet analysis Telecommunications

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A Comparison of Bayesian Estimators for the Parameters of the Bivariate Multifractal Spectrum 30

A Comparison of Bayesian Estimators for the Parameters of th...

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30th European signal processing conference (EUSIPCO)

作者： Leon, Lorena Wendt, Herwig Tourneret, Jean-Yves Abry, Patrice Univ Toulouse Inst Rech Informat Toulouse IRIT CNRS Toulouse INPUT3UT2 Toulouse France Univ Lyon CNRS Lab Phys ENS LyonUniv Claude Bernard Lyon France

ISBN: (纸本)9789082797091

Multifractal analysis provides the theoretical and practical tools for describing the fluctuations of pointwise regularity in data and has led to many successful applications in signal and image processing. Originally limited to the analysis of single time series or images, a definition of multivariate multifractal analysis, i.e., the joint multifractal analysis of several data components, was recently proposed and was shown to effectively quantify local or transient dependencies in data regularity, beyond linear correlation. However, the accurate estimation of the associated matrix-valued joint multifractality parameters is notoriously difficult, thus limiting its practical usefulness. Leveraging a recent statistical model for bivariate multifractality, the goal of this work is to define and study Bayesian estimators designed to bypass this difficulty. Specifically, we study the original use of two different priors, combined with two different averages (arithmetic and Karcher means), for bivariate multifractal analysis. Monte Carlo simulations with synthetic data allow us to appreciate their relative performance and to conclude that our novel and original estimator based on a scaled inverse Wishart prior and the Karcher mean yields particularly favorable results with up to 5 times smaller root-mean-squared error than previous formulations.

关键词： Multifractal analysis multivariate data Bayesian estimation wavelet leaders Karcher mean

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Hybrid Watermarking Algorithm to Protect and Authenticate KhalifaSat imagery using DWT-SVD and SHA3 Hash Key 5

Hybrid Watermarking Algorithm to Protect and Authenticate Kh...

引用

5th IEEE International conference on image processing applications and Systems (IPAS)

作者： Panthakkan, Alavikunhu Anzar, S. M. Al Mansoori, Saeed Al-Ahmad, Hussain Univ Dubai Coll Engn & IT Dubai U Arab Emirates TKM Coll Engn Dept Elect & Commun Kollam Kerala India Mohammed Bin Rashid Space Ctr Applicat Dev Sect Dubai U Arab Emirates

ISBN: (纸本)9781665462198

Using DWT-SVD and SHA3 Hash function, this research aims to develop an ownership protection and image authentication technique that embeds the watermark information and hash authentication key in a hybrid domain. The experiment was conducted with multispectral images from the KhalifaSat. The Performance of the proposed method is evaluated using wavelet domain signal to noise ratio (WSNR), structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR). To analyse the efficacy of the recovered watermark, two metrics are used: Normalized Correlation (NC) and image Quality Index (IQI). The method presented is robust against many intended and unintended attacks. Without sacrificing transparency, our proposed watermarking approach meets the objectives of imperceptibility and robustness. It accurately detects the manipulated locations on the satellite image and is sensitive to even small changes.

关键词： Digital watermarking Hash Function image Authentication Ownership Protection

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Fusion model of Modified wavelet Transform and Neural Network for medical image compression 3

Fusion model of Modified Wavelet Transform and Neural Networ...

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3rd International conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Shyamala, N. Geetha, S. Nallamuthu Gounder Mahalingam College Ug Department of Computer Applications Pollachi642002 India Government Arts and Science College for Women Department of Computer Science Puliakulam CBE-641045 India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Medical imaging is one of emerging fields that has high impact on analysis and diagnosis of diseases. image compression is introduced in the medical image processing to solve memory and bandwidth requirement problems. In this paper, a hybrid image compression method by integrating Modified wavelet Transform (MWT) and Neural Network (NN) is proposed for efficient storage as well as transmission of medical images. Primarily, medical images are preprocessed using median filter to suppress noisy data and then decomposed into approximation and detailed coefficients using wavelet transform. Subsequently, detailed coefficients are filtered with proposed thresholding technique. Both approximation and detailed coefficients are divided into sub matrix with the size of n x n and used as input to the NN to perform compression. The image compression performance of the proposed method is evaluated by computing peak signal to noise ratio, compression ratio and storage space and compared with prevailing methods to prove its effectiveness. The empirical findings reveal that the proposed method provides best performance compared to the former methods taken for comparison. © 2022 IEEE.

关键词： wavelet transforms

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An Analysis Study of Various image Preprocessing Filtering Techniques based on PSNR for Leaf images

An Analysis Study of Various Image Preprocessing Filtering T...

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2022 International conference on Advanced Computing Technologies and applications, ICACTA 2022

作者： Dhivya, R. Shanmugapriya, N. Dr. SNS Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Coimbatore India

ISBN: (纸本)9781665495158

The noise would be a significant element that affects the quality of leaf images. The level of valuable features that could be extracted from the image has frequently been reduced by the level of noise, also some essential image sections are most often distorted. image noise has been experimented with by several analysts as the spontaneous variance of illumination or color details within images leads to noises while acquiring. Noise in a leaf image has been the outcome of different forms of errors induced by multiple causes such as the atmosphere and also the instruments involved and is added as a result of errors that arise during processing the image, encoding, and storing. Mainly the effect of Gaussian-Noise (GN) induces higher or lower contrast in both the edge region of the input image that degrades the quality of the leaf images. This research article discusses the strategies and procedures for removing noise from leaf images. The primary objective here would be to upgrade the quality of the leaf image by preprocessing for improving the performance of the automated Leaf Disease Detection (LDD) model. In this research, we propose the following filtering techniques for preprocessing the leaf image 'Discrete-Cosine-Transform (DCT)', 'Discrete-wavelet-Transform (DWT)', and 'K-means Singular-Value-Decomposition and DWT (K-SVD-DWT)'. The superior filtering approach was determined using the metric 'Peak-signal-to-Noise-Ratio (PSNR)'. The outcome of the highest PSNR denoised image can be transmitted into the segmentation task for further LDD process. © 2022 IEEE.

关键词： Discrete wavelet transforms

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Kinship Verification Using Multiscale Retinex Preprocessing and Integrated 2DSWT-CNN Features

Kinship Verification Using Multiscale Retinex Preprocessing ...

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International conference on image and signal processing and their applications (ISPA)

作者： El Ouanas Belabbaci Mohammed Khammari Ammar Chouchane Abdelmalik Ouamane Mohcene Bessaoudi LIMED Laboratory Faculty of Technology University of Bejaia Bejaia Algeria LIMED Laboratory Faculty of Exact Sciences University of Bejaia Bejaia Algeria Faculty of Sciences and Technology University Center of Barika Barika Algeria Laboratory of LI3C University Mohamed Khider of Biskra Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Kinship verification from facial images presents a challenging yet intriguing problem within the fields of pattern recognition and computer vision. In this study, we introduce significant advancements by applying a preprocessing technique known as Multiscale Retinex with Color Restoration (MSRCR) to enhance the quality and contrast of images. Our methodology uniquely combines the strengths of both deep and shallow feature descriptors by integrating them at the score level through the use of Logistic Regression (LR). Specifically, we utilize a novel descriptor, Histograms of a Two-Dimensional Stationary wavelet Transform (Hist-2D-SWT), to capture non-deep features, while employing the CNN model for deep feature extraction. The efficacy of our approach is thoroughly evaluated through extensive experiments conducted on three challenging kinship datasets: Cornell KinFace, UB KinFace, and TS KinFace.

关键词： wavelet transforms Logistic regression Histograms Computer vision image color analysis signal processing Feature extraction

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An Approach for CT image Conversion Using Filtering Based on Quaternion Mathematics 13th

An Approach for CT Image Conversion Using Filtering Based o...

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13th International conference on image processing and Communications, IP and C 2023, 13th International conference on Computer Recognition Systems, CORES 2023

作者： Nasr, Mahmoud Piórkowski, Adam Brzostowski, Krzysztof El-Samie, Fathi E. Abd Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology A. Mickiewicza 30 Av Cracow30-059 Poland New Cairo11845 Egypt Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Manoufia University Menouf Egypt

ISBN: (纸本)9783031416293

Computed tomography (CT) images are widely used in medical examination applications. They are constructed from the raw data using different kernels. However, due to the technology used, the software finds problems with image segmentation in the presence of noise or blurred edges. In this paper, we use quaternion mathematics for image de-noising by applying the bilateral filtering algorithm to reconstruct a pure soft image from the images reconstructed with sharp kernels, without using the raw data. Our output is compared to reconstructed images with different denoising techniques, such as non-local means and wavelet denoising. The reconstructed images using other techniques are assessed using structural index similarity (SSIM) and peak signal-to-noise ratio (PSNR). Results show high similarity by comparing the outputs of sharp kernels to that of the B30f. Furthermore, the results show that the proposed work can efficiently increase the similarity between the images reconstructed with hard and soft kernels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image denoising

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A Speech Enhancement Algorithm Combining wavelet Transform and Adaptive Filters

A Speech Enhancement Algorithm Combining Wavelet Transform a...

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International image processing, applications and Systems conference (IPAS)

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

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

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.

关键词： wavelet transforms Additive noise Wiener filters Filtering Noise Urban areas Adaptive filters Interference Speech enhancement wavelet analysis

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