检索结果-内蒙古大学图书馆

Recent progress and applications of Raman spectrum denoising algorithms in chemical and biological analyses: A review

引用

TRAC-TRENDS IN ANALYTICAL CHEMISTRY 2024年 172卷

作者： Fang, Shiyan Wu, Siyi Chen, Zhou He, Chang Li Lin, Linley Ye, Jian Shanghai Jiao Tong Univ Sch Biomed Engn State Key Lab Syst Med Canc Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Inst & Med Robot Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Ren Ji Hosp Sch Med Shanghai Key Lab Gynecol Oncol Shanghai 200127 Peoples R China

Raman spectroscopy is a powerful technique widely used in analytical chemistry. However, spectral noise emerging during detection introduces potential to compromise the signal-to-noise ratio, undermining the accuracy and reliability of sample analyses. This limitation has driven the development of Raman spectrum denoising algorithms to encourage the application of Raman spectroscopy in more complicated domains of analytical chemistry, including unknown compound identification, trace detection, single -particle sensing, ultrafast imaging, and in-depth in vivo detections. In this review, we outline the essential concepts of Raman spectroscopy, the origins of spectral noise and noise reduction through various strategies. Furthermore, we present a comprehensive summary of Raman spectral denoising algorithms and their progressions in three categories of moving window smoothing, power spectrum estimation, and deep learning algorithms. Finally, challenges and future directions in denoising algorithms are discussed. This review severs as a valuable resource to shed light on algorithmic solutions to enhance Raman spectroscopy analysis.

关键词： Raman spectroscopy denoising algorithms Filter Power spectrum estimation Deep learning

来源：评论

学校读者我要写书评

暂无评论

A reliable evaluation approach for multichannel signal denoising algorithms based on a novel arterial pulse acquisition system

引用

HELIYON 2024年第5期10卷 e26140页

作者： Chen, Chao Chen, Zhendong Zhou, Yuqi Hao, Yinan Peng, Bo Xie, Xiaohua Xie, Haiqing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Dept Pulm & Crit Care Med Guangzhou 510630 Peoples R China Xinghai Conservatory Mus Dept Mus Instrument Engn Guangzhou 510006 Peoples R China Sniow Res & Dev Lab Foshan 528000 Peoples R China Foshan Univ Sch Med Engn Foshan 528000 Peoples R China Guangzhou Higher Educ Mega Ctr 398 Outer Ring West Rd Guangzhou Guangdong Peoples R China

Background: Tactile sensors are utilized to measure multichannel pulse signals in pulse wave analysis (PWA). Owing to noise interferences, researchers have applied various denoising algorithms on multichannel pulse signals. To comprehensively assess these algorithms, numerous evaluation metrics have been proposed. However, these studies did not investigate the noise mechanisms in depth and lacked reference pulse signals, thus making the evaluations insufficiently objective. Materials and methods: An applicable denoising evaluation approach for multichannel pulse signal algorithms based on an arterial pulse acquisition system is established by superimposing realworld multichannel noise to the reference signals. The system, comprising a SphygmoCor and a uniaxial noise acquisition device, allows us to acquire single-reference pulse signals as well as real-world multichannel noise. Results: We assess eight popular denoising algorithms with three evaluation metrics, including amplitude relative error (ARE), mean square error (MSE) and increased percentage signal-noise ratio (SNR%). Our proposed approach provides accurate and objective evaluations of multichannel pulse signal denoising. Notably, classic algorithms for single-channel denoising are not recommended for multichannel denoising. Comparatively, RPCA-based algorithms can denoise pulse signals independently for each channel. Conclusion: This study sets the stage for the establishment of accurate and objective pulse signal denoising evaluations and provides insights for data-driven clinical diagnoses in cardiovascular medicine.

关键词： Biomedical engineering Pulse wave analysis Multichannel pulse signals Evaluation approach Noise reduction denoising algorithms Cardiovascular medicine

来源：评论

学校读者我要写书评

暂无评论

Evaluation of denoising and Voxelization algorithms on 3D Point Clouds

引用

REMOTE SENSING 2024年第14期16卷 2632页

作者： Gonizzi Barsanti, Sara Marini, Marco Raoul Malatesta, Saverio Giulio Rossi, Adriana Univ Campania Luigi Vanvitelli Engn Dept Via Roma 9 I-81031 Aversa Italy Sapienza Univ Rome Comp Sci Dept Via Salaria 113 I-00198 Rome Italy Sapienza Univ Rome Interdept Res Ctr DigiLab Via Volsci 122 I-00185 Rome Italy

Proper documentation is fundamental to providing structural health monitoring, damage identification and failure assessment for Cultural Heritage (CH). Three-dimensional models from photogrammetric and laser scanning surveys usually provide 3D point clouds that can be converted into meshes. The point clouds usually contain noise data due to different causes: non-cooperative material or surfaces, bad lighting, complex geometry and low accuracy of the instruments utilized. Point cloud denoising has become one of the hot topics of 3D geometric data processing, removing these noise data to recover the ground-truth point cloud and adding smoothing to the ideal surface. These cleaned point clouds can be converted in volumes with different algorithms, suitable for different uses, mainly for structural analysis. This paper aimed to analyse the geometric accuracy of algorithms available for the conversion of 3D point clouds into volumetric models that can be used for structural analyses through the FEA process. The process is evaluated, highlighting problems and difficulties that lie in poor reconstruction results of volumes from denoised point clouds due to the geometric complexity of the objects.

关键词： 3D reality-based modelling geometrical analysis retopology denoising algorithms voxel NURBS FEA

来源：评论

学校读者我要写书评

暂无评论

Rendered image denoising method with filtering guided by lighting information

引用

Optoelectronics Letters 2025年第4期21卷 242-248页

作者： MA Minghui HU Xiaojuan ZHANG Ripei CHEN Chunyi YU Haiyang School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China

The visual noise of each light intensity area is different when the image is drawn by Monte Carlo ***,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed *** we propose a rendered image denoising method with filtering guided by lighting ***,we design an image segmentation algorithm based on lighting information to segment the image into different illumination ***,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination *** different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area ***,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the *** the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on *** shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.

关键词： establish paramet rendered image denoising Monte Carlo method filtering guided lighting information denoising algorithms image segmentation algorithm rendered image denoising method monte carlo methodhoweverthe

来源：评论

学校读者我要写书评

暂无评论

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

引用

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2020年第3期34卷 354-371页

作者： Ashrarf, M. Said Ashraf, A. M. Khalaf Fac Engn Biomed Engn Dept Al Minya 61111 Egypt Fac Engn Dept Elect & Commun Al Minya Egypt

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise-cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well-known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step-size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post-signal-to-noise ratio, mean square error, and percentage root mean square difference.

关键词： adaptive noise canceller denoising algorithms discrete wavelet transform ECG signals neural network partial update

来源：评论

学校读者我要写书评

暂无评论

FPGA-accelerated anisotropic diffusion filter based on SW/HW-codesign for medical images

引用

JOURNAL OF REAL-TIME IMAGE PROCESSING 2021年第6期18卷 2429-2440页

作者： Hadj Fredj, Amira Malek, Jihene Monastir Univ Fac Sci Monastir Lab Elect & Microelect Monastir Tunisia Sousse Univ Higher Inst Appl Sci & Technol Sousse Sousse Tunisia

In medical imaging, denoising is very important for the analysis of images and the diagnosis and treatment of diseases. Currently, the image denoising methods based on anisotropic diffusion are efficient. However, the methods have been limited as regards the processing time. In recent computing systems, the FPGA-based acceleration has been highly competitive for GPU-based one due to its high computation capabilities and lower energy consumption. In this paper, we present a high-level synthesis implementation on a SOC-FPGA of an anisotropic diffusion algorithm dedicated to medical applications. We choose an oriented speckle reducing anisotropic diffusion denoising filter, which provides robust performance but requires a significant computation on the embedded CPU since it is iterative. Moreover, we optimize the performance by modifying the original algorithm, automizing it by controlling the diffusion process at each iteration, and accelerating the processing operations by providing a hardware/software description. The evaluation is performed using different medical images. The efficiency and relevance of the proposed filter is demonstrated through segmentation. The design is validated on FPGA XC7Z020CLG484-1 with a frequency of 255 MHz and a PSNR of about 30 dB.

关键词： denoising algorithms Anisotropic diffusion filter Segmentation Medical application SW HW codesign ZYNQ-7 ZC702

来源：评论

学校读者我要写书评

暂无评论

Numerical Comparisons of Different Imaging algorithms

引用

Journal of Applied Mathematics and Physics 2023年第9期11卷 2671-2690页

作者： Soulef Bougueroua Nourreddine Daili Department of Mathematics Faculty of Sciences University Ferhat Abbas Setif 1 Setif Algeria Fundamental and Numerical Mathematics Laboratory University Ferhat Abbas Setif 1 Setif Algeria

Image processing is the set of operations performed to extract “information” from the image. An interesting problem in digital image processing is the restoration of degraded images. It often happens that the resulting image is different from the expected image. Our problem will therefore be to recover an image close to the original image from a poor quality image (that has been skewed by Gaussian and additive noise). There are several algorithms on how we can improve the broken image in better quality. We present in this paper our numerical results obtained with the models of Tikhonov regularization, ROF, Vese Osher, anisotropic and isotropic TV denoising algorithms.

关键词： Comparative denoising algorithms Image Restoration

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Filters for Noise Reduction and Smoothening of Brain MRI Images 6

A Comparative Analysis of Filters for Noise Reduction and Sm...

引用

6th International Conference for Convergence in Technology (I2CT)

作者： Shedbalkar, Jayashree Prabhushetty, K. Inchalc, Abhayakumar Sgbit Belagavi 590018 Karnataka India Sgbit ECE Belagavi 590018 Karnataka India KLSs VDIT CSE Haliyal 581329 Karnataka India

ISBN: (纸本)9781728188768

Image pre-processing is used to improve the quality of image by removing irrelevant and unwanted parts of the image such as noise. Medical images generally suffer from noise, and this noisy effect is due to the inherent properties of the medical devices such as magnetic field generated by the scanners or may be due to the vibrations and loud sound generated during Magnetic Resonance Image (MRI) scan. MRI images suffer due to radiation and physical movements of magnetic parts of the devices which are responsible for image noise in MRI. The MRI device uses strong magnetic field to create images and hence images usually suffer from different types of noises such as Gaussian, Salt and pepper, speckle etc. Removing such noise from brain MRI images is a big challenge for researchers to have better visualization of the images to accurately identify brain tumor. The core objective of this paper is to discuss different methods used in pre-processing of the brain MRI images and compare the quality of the resultant images generated by different pre-processing techniques. This paper discusses different types of noises and filters suitable for these noises by comparing the histograms of various filters.

关键词： Pre-Processing MRI Image denoising algorithms Filtering Techniques Histograms

来源：评论

学校读者我要写书评

暂无评论

Solving RED With Weighted Proximal Methods

引用

IEEE SIGNAL PROCESSING LETTERS 2020年第0期27卷 501-505页

作者： Hong, Tao Yavneh, Irad Zibulevsky, Michael Technion Israel Inst Technol Dept Comp Sci IL-3200003 Haifa Israel

REgularization by denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors. A drawback of this approach is the high computational complexity of denoisers, which dominate the computation time. In this paper, we apply a general framework called weighted proximal methods (WPMs) to solve RED efficiently. We first show that two recently introduced RED solvers (using the fixed point and accelerated proximal gradient methods) are particular cases of WPMs. Then we show by numerical experiments that slightly more sophisticated variants of WPM can lead to reduced run times for RED by requiring a significantly smaller number of calls to the denoiser.

关键词： Signal processing algorithms Noise reduction Inverse problems Acceleration Approximation algorithms Minimization Task analysis Inverse problem denoising algorithms RED weighted proximal methods weighting

来源：评论

学校读者我要写书评

暂无评论

Adaptive Signal Processing algorithms Based on EMD and ITD

引用

IEEE ACCESS 2019年 7卷 171313-171321页

作者： Voznesensky, Alexander Kaplun, Dmitrii St Petersburg Electrotech Univ LETI Dept Automat & Control Proc St Petersburg 197376 Russia

In the last two decades, both Empirical Mode Decomposition (EMD) and Intrinsic Time-Scale Decomposition (ITD) algorithms deserved a variety of applications in various fields of science and engineering due to their obvious advantages compared to conventional (e.g. correlation- or spectral-based analysis) approaches like the ability of their direct application to non-stationary signal analysis. However, high computational complexity remains a common drawback of these otherwise universal and powerful algorithms. Here we compare similarly designed signal analysis algorithms utilizing either EMD or ITD as their core functions. Based on extensive computer simulations, we show explicitly that the replacement of EMD by ITD in several otherwise similar signal analysis scenarios leads to the increased noise robustness with simultaneous considerable reduction of the processing time. We also demonstrate that the proposed algorithms modifications could be successfully utilized in a series of emerging applications for processing of non-stationary signals.

关键词： Adaptive filter bank denoising algorithms empirical mode decomposition Fourier transform Hilbert-Huang transform internal oscillations intrinsic time-scale decomposition spectral analysis wavelet transform

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：