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中国激光 2024年第14期51卷 134-143页

作者：冯沐祺徐启明战宇刘常升东北大学材料科学与工程学院辽宁沈阳110819 东北大学信息科学与工程学院辽宁沈阳110819 东北大学理学院辽宁沈阳110819

为实现轨道钢表面裂纹的快速原位定位定量检测,提出基于激光超声技术的双侧扫描成像方法。在裂纹两侧进行了激光超声线扫描检测,采用小波包变换的方法对波形数据进行了降噪处理,对降噪后的数据进行B扫描成像处理,并对整体裂纹进行了成... 详细信息

为实现轨道钢表面裂纹的快速原位定位定量检测,提出基于激光超声技术的双侧扫描成像方法。在裂纹两侧进行了激光超声线扫描检测,采用小波包变换的方法对波形数据进行了降噪处理,对降噪后的数据进行B扫描成像处理,并对整体裂纹进行了成像。针对线扫描成像法检测裂纹长度误差较大的问题,提出了绘制扫查位置-最大幅值差图的方法以确定裂纹的起始和终止位置,从而实现裂纹长度信息的获取,提高裂纹长度检测的精度。结果表明,双侧扫描成像法可直观地得到裂纹的位置、长度和宽度信息。其中,该方法检测裂纹位置的相对误差不超过0.83%,检测裂纹宽度的相对误差为1.56%,检测裂纹长度的相对误差为8%。绘制扫查位置-最大幅值差图的方法检测裂纹长度的相对误差不超过4%,明显高于双侧扫描成像法。所提方法在金属表面裂纹的快速成像检测方面具有可行性,且为激光超声检测裂纹长度提供了更精确的数据处理方案。

关键词：测量激光超声缺陷检测 B扫描小波包降噪

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Airbus Ship Detection from Satellite imagery using Frequency Domain Learning 27

Airbus Ship Detection from Satellite Imagery using Frequency...

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Conference on image and signal processing for Remote Sensing XXVII

作者： Al-Saad, Mina Aburaed, Nour Panthakkan, Alavikunhu Al Mansoori, Saeed Al Ahmad, Hussain Marshall, Stephen Univ Dubai Dubai U Arab Emirates Mohammed Bin Rashid Space Ctr Dubai U Arab Emirates Univ Strathclyde Glasgow Lanark Scotland

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Ship detection from remote sensing images has been a topic of interest that gradually gained attention over the years due to the wide variety of its applications in the field of maritime surveillance, such as oil discharge control, sea pollution monitoring, and harbour management. Even though there is an extensive amount of methods developed for ship detection, there are still several challenges that remain unsolved, especially in complex environments. These challenges include occlusions due to shadows, clouds, and fog. Nowadays, deep learning algorithms, especially Deep Convolutional Neural Networks (DCNNs), are considered as a powerful approach for automatically detecting ships in satellite imagery. In this paper, enhanced Faster R-CNN (FRCNN) model will be used to overcome the aforementioned unsolved challenges. The enhanced FRCNN, which combines high level features with low level features, will be trained and tested in the frequency domain using the publicly available satellite imagery dataset, Airbus Ship Detection, provided by Kaggle. The performance will be compared to the original FRCNN based on their Overall Accuracy (OA) and Mean Average Precision (mAP) metrics.

关键词： Deep Learning FRCNN Discrete wavelet Transform Remote Sensing Ship Detection

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Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT images

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Journal of Biomedical Physics and Engineering 2022年第6期12卷 655-668页

作者： Mahmoudi, Tahereh Radmard, Amir Reza Salehnia, Aneseh Ahmadian, Alireza Davarpanah, Amir H. Kafieh, Ra-Heleh Arabalibeik, Hossein Department of Medical Physics and Biomedical Engineering School of Medicine Tehran University of Medical Sciences Tehran Iran Imam Khomeini Hospital Complex Tehran University of Medical Sciences Tehran Iran Department of Radiology Shariati Hospital Tehran University of Medical Sciences Tehran Iran Department of Radiology and Imaging Sciences Emory University School of Medicine 1364 Clifton Rd NE AtlantaGA30322 United States Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective: This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods: This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classi-fier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results: The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion: Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications. © Journal of Biomedical Physics and Engineering.

关键词： Tumors

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基于小波变换与深度残差融合的图像增强算法

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光电子．激光 2022年第7期33卷 715-722页

作者：樊文定李彬华李俊武昆明理工大学信息工程与自动化学院云南昆明650500 昆明理工大学云南省计算机应用技术重点实验室云南昆明650500

针对目前基于小波变换图像融合增强算法原始图像中的多尺度细节信息的不足,提出了一种改进的多尺度小波变换与深度残差选择相结合的图像增强算法。利用小波变换对原始图像进行分解提取得到它的多级分解系数后,再利用不同规则对不同层次... 详细信息

针对目前基于小波变换图像融合增强算法原始图像中的多尺度细节信息的不足,提出了一种改进的多尺度小波变换与深度残差选择相结合的图像增强算法。利用小波变换对原始图像进行分解提取得到它的多级分解系数后,再利用不同规则对不同层次的小波系数进行重构,与此同时引入深度残差算法的思想对子带系数做残差。对于高频子带系数,计算子带残差的系数与梯度特征融合方法的系数,选用两者最大值进行融合增强;而对于低频子带系数则采用梯度特征融合增强系数与子带残差系数取平均值的算法进行融合。通过在MATLAB平台上的实验对所提出算法进行验证,峰值信噪比相较于对比的方法都有所提高,且均方根误差也得到减小,结构相似度都得到提高,结果表明该算法能增强图像的多尺度细节信息,提高图像的信噪比,且具有更好的图像增强效果。

关键词：数字图像处理信息增强深度残差多尺度分析

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Graph-Based Compensated wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data

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IEEE TRANSACTIONS ON image processing 2020年 29卷 2439-2451页

作者： Lanz, Daniela Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Chair Multimedia Commun & Signal Proc D-91058 Erlangen Germany

Lossless compression of dynamic 2D & x002B;t and 3D & x002B;t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently introduced to this framework. However, encoding the motion information, which is stored in adjacency matrices, is not well investigated so far. This work focuses on coding these adjacency matrices to make the graph-based motion compensation feasible for data compression. We propose a novel coding scheme based on constructing so-called motion maps. This allows for the first time to compare the performance of graph-based motion compensation to traditional block- and mesh-based approaches. For high quality lowpass subbands our method is able to outperform the block- and mesh-based approaches by increasing the visual quality in terms of PSNR by 0.53dB and 0.28dB for CT data, as well as 1.04dB and 1.90dB for MR data, respectively, while the bit rate is reduced at the same time.

关键词： image coding Biomedical imaging Computed tomography Transforms Encoding Motion compensation Scalability Scalability discrete wavelet transform motion compensation graph-based signal processing computed tomography magnetic resonance imaging

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image Enhancement Using HSV Color Space, DWT, and BiHE Techniques

Image Enhancement Using HSV Color Space, DWT, and BiHE Techn...

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International Conference on Engineering Technology and their applications (IICETA)

作者： Tameem Hameed Obaida Salman Abd Kadum Fallah H. Najjar Hassan M. Al-Jawahry Technical Institute of Najaf Al-Furat Al-Awsat Technical University Najaf Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Faculty of Engineering The Islamic University Najaf Iraq

image enhancement is vital in computer vision and image processing applications. This study introduces an innovative approach combining HSV color space, Discrete wavelet Transforms (DWT), and Bi Histogram Equalization (BiHE) techniques. By converting the image from RGB to HSV, we separate color and intensity information, allowing us to enhance the intensity component for improved visual quality. The DWT is then applied to decompose the image into frequency sub bands, enabling targeted manipulation of specific frequency components to enhance image details and overall appearance. Additionally, the BiHE technique enhances contrast while preserving local image details. The proposed method was evaluated on various images, including tissue, football, Lena, and yellow lily. The results show that the proposed method achieved high Peak signal-to-Noise Ratio (PSNR) values, with tissue achieving 32.1853 dB, football achieving 30.5452 dB, Lena achieving 27.1462 dB, and yellow lily achieving 25.2015 dB. The Mean Squared Error (MSE) values were also low, with tissue having an MSE of 0.0006, football having an MSE of 0.0009, Lena having an MSE of 0.0022, and yellow lily having an MSE of 0.0031. These results demonstrate the effectiveness of the proposed method in enhancing the visual quality, details, and contrast of the images while maintaining naturalness and avoiding artifacts. Experimental results demonstrate that our method outperforms traditional techniques, significantly enhancing visual quality, details, and contrast while maintaining naturalness and avoiding artifacts.

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EXTENSION OF HAAR wavelet TECHNIQUES FOR MITTAG-LEFFLER TYPE FRACTIONAL FREDHOLM INTEGRO-DIFFERENTIAL EQUATIONS

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第2期31卷

作者： Reunsumrit, Jiraporn Shah, Kamal Khan, Aziz Amin, Rohul Ahmad, Israr Sitthiwirattham, Thanin King Mongkuts Univ Technol North Bangkok Fac Appl Sci Dept Math Bangkok 10800 Thailand Prince Sultan Univ Dept Math & Sci Riyadh 11586 Saudi Arabia Univ Malakand Dept Math Khyber Pakhtunkhwa 18000 Pakistan Univ Peshawar Dept Math Khyber Pakhtunkhwa Pakistan Govt Post Grad Jahanzeb Coll Dept Math Khyber Pakhtunkhwa Pakistan Suan Dusit Univ Fac Sci & Technol Math Dept Bangkok 10300 Thailand

Fractional order integro-differential equation (FOIDE) of Fredholm type is considered in this paper. The mentioned equations have many applications in mathematical modeling of real world phenomenon like image and signal processing. Keeping the aforementioned importance, we study the considered problem from two different aspects which include the existence theory and computation of numerical approximate solution. FOIDEs have been investigated very well by using Caputo-type derivative for the existence theory and numerical solutions. But the mentioned problems have very rarely considered under the Mittage-Leffler-type derivative. Also, for FOIDE of Fredholm type under Mittage-Leffler-type derivative has not yet treated by using Haar wavelet (HW) method. The aforementioned derivative is non-singular and nonlocal in nature as compared to classical Caputo derivative of fractional order. In many cases, the nonsingular nature is helpful in numerical computation. Therefore, we develop the existence theory for the considered problem by using fixed point theory. Sufficient conditions are established which demonstrate the existence and uniqueness of solution to the proposed problem. Further on utilizing HW method, a numerical scheme is developed to compute the approximate solution. Various numerical examples are given to demonstrate the applicability of our results. Also, comparison between exact and numerical solution for various fractional orders in the considered examples is given. Numerical results are displayed graphically.

关键词： FOIDEs Fixed Point Theory HW Technique Numerical Solution

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An adaptive self-guided wavelet convolutional neural network with compound loss for low-dose CT denoising

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BIOMEDICAL signal processing AND CONTROL 2022年第0期75卷 103543-103543页

作者： Li, Saize Li, Qing Li, Runrui Wu, Wei Zhao, Juanjuan Qiang, Yan Tian, Yuling Taiyuan Univ Technol Coll Informat & Comp Taiyuan 030600 Peoples R China Shanxi Prov Peoples Hosp Taiyuan 030012 Peoples R China

Computed Tomography (CT) is an imaging method widely used in clinical, industrial, and other applications. Furthermore, it is one of the common methods of modern clinical medical imaging diagnosis. However, excessive radiation doses in CT scans can cause harm to the human body. Reducing the radiation dose will cause serious degradation of the reconstructed image quality, produce speckle noise and streak artifacts, and affect the accuracy of clinical medical diagnosis. In order to obtain high-quality images while reducing CT radiation dose, we propose an adaptive self-guided wavelet convolutional neural network (ASWCNN) to convert low-dose CT (LDCT) images into normal-dose CT (NDCT) images as true as possible. In our ASWCNN, combining wavelet transform and sub-pixel convolution, a top-down self-guiding structure is proposed as the overall architecture of the network. An adjustable pyramid residual block (APRB) is proposed to self-adaption extract multi-scale and diversity information features as image resolution decreases along with the network. The adjacent scale information fusion block (ASIFB) is proposed to fuse the information features between adjacent scales step by step to improve the stability of network training. At the same time, we use the cross-latitude mixed attention block (CMAB) as the feature enhancement block of our network to enhance the fused feature information, enhance the effective information and suppress the useless information. In addition, we proposed the correction reconstruction block (CRB) to reduce the gap between the obtained denoised CT image and the given NDCT image. Finally, we take the compound loss combining the pixel-level loss, structure-perceived loss, and gradient loss as our loss function. Experimental results show that our proposed method can effectively retain the structure and texture information of CT images while removing noise and artifacts. At the same time, compared with other methods, better subjective visual evaluati

关键词： Low-dose CT wavelet transform CNN Multi-scale and diversity Compound loss

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DWT-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications

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signal image AND VIDEO processing 2020年第3期14卷 601-608页

作者： Jose, Shobha Thomas George, S. Roopchand, P. S. Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India Karunya Inst Technol & Sci Dept Biomed Engn Coimbatore Tamil Nadu India St Thomas Hosp Dept Neurol Kottayam Kerala India

Automated diagnosis of neuromuscular disorders such as myopathy and neuropathy can be done by measuring and analyzing the nonlinear and non-stationary trends in electromyogram (EMG) signals. This paper introduces a new automated diagnostic approach with the combination of discrete wavelet transform (DWT) and maximum likelihood estimation (ML-estimation) for analysis and classification of EMG signals into healthy, myopathy, or neuropathy classes. DWT decomposes the considered EMG signals into frequency sub-bands, and ML-estimation is used for extracting the features from the discrete wavelet sub-bands. Subsequently, obtained feature vectors are used for classifying the EMG signals using multilayer perceptron neural network (MLPNN). Comparative analysis of various performance measures has been performed with all the 71 wavelets of DWT and 13 training algorithms of MLPNN. Results are promising with specificity of 83.33%, sensitivity (myopathy) of 91.66%, sensitivity (neuropathy) of 83.33%, and total classification accuracy of 86.5%, which are obtained using the combination of Daubechies-11 wavelet and Fletcher-Reeves conjugate gradient training algorithm.

关键词： Discrete wavelet transform (DWT) Electromyogram (EMG) Maximum likelihood estimation (ML-estimation) Multilayer perceptron neural network (MLPNN) Neuromuscular disorders

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On the 2D Post-processing of Brillouin Optical Time-Domain Analysis

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2020年第14期38卷 3723-3736页

作者： Zaslawski, Simon Yang, Zhisheng Thevenaz, Luc Swiss Fed Inst Technol Lausanne Inst Elect Engn CH-1015 Lausanne Switzerland

The benefits and limitations inherent to the 2D postprocessing of measurements from Brillouin optical time-domain analyzers are investigated from a fundamental point of view. In a preliminary step, the impact of curve fitting on the precision of the estimated Brillouin frequency shift is analyzed, enabling a fair comparison between the representative noise-reduction algorithms studied in this article. The performances in terms of signal-to-noise ratio, experimental uncertainty sB on the Brillouin frequency shift and spatial resolution delivered by advanced image processing methods-such as wavelet transform and non-local means algorithms-are then compared with the impact of a 2D Gaussian filter. The major discrepancies observed when comparing the gain in signal-to-noise ratio to the sB reduction are then determined by exploiting the separability of the Gaussian filter, which reveals that noise reduction is only effective along 1-D of the 2D array of measurements and originates froma digital reduction of the system analog bandwidth. The signal-to-noise ratio improvement obtained from filtering in the spectral dimension is only illusory, since its action is redundant with the curve fitting procedure to estimate the Brillouin frequency shift. Finally, the maximum sB reduction achievable by digital post-processing is theoretically given, hence setting a fundamental limit to the improvement brought by data processing.

关键词： Scattering signal to noise ratio Optical pulses Optical variables measurement Optical pumping Optical sensors Optical imaging Brillouin scattering optical fibers signal processing optical fiber measurement applications

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