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A denoising algorithm for an electromagnetic acoustic transducer (EMAT) signal by envelope regulation

MEASUREMENT SCIENCE AND TECHNOLOGY 2010年第8期21卷 085206-085206页

作者： Huang, Songling Tong, Yun Zhao, Wei Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

To reduce the noise in the inspection data of an electromagnetic acoustic transducer (EMAT), a denoising method using an envelope regulation technique is proposed. A cubic polynomial interpolation curve fitting method is used to obtain the envelope of the original signal. The upper envelope and the lower envelope are used separately to modify the original signal according to a chosen denoising threshold. The upper and lower modified signals are averaged to get the final result. The algorithm can suppress small-amplitude high-frequency noises mixed in the signal, without damaging the main feature of the signal. The algorithm is tested by an experiment with EMAT surface wave data. The result shows that the denoising algorithm based on envelope regulation can effectively eliminate the clutter and burrs in the inspection data, and improve the signal-to-noise ratio.

关键词： electromagnetic acoustic transducer (EMAT) denoising algorithm envelope regulation Hermite interpolation signal-to-noise ratio (SNR)

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A 2D image 3D reconstruction function adaptive denoising algorithm

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1604页

作者： Wang, Feng Ni, Weichuan Liu, Shaojiang Xu, Zhiming Qiu, Zemin Wan, Zhiping Guangzhou Xinhua Univ Dongguan Guangdong Peoples R China

To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image's detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target's feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction.

关键词： denoising algorithm Threshold Adversarial generative network 3D reconstruction

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The impact of improved non-local means denoising algorithm on photon-counting X-ray images using various Al additive filtrations

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2022年 1027卷 166244-166244页

作者： Lee, Seungwan Lee, Youngjin Konyang Univ Dept Radiol Sci 158 Gwanjeodong Ro Daejeon South Korea Gachon Univ Dept Radiol Sci 191 Hambakmoero Incheo South Korea

The filters used in X-ray systems affect the image quality and radiation dose. Although using filters has advantages in terms of image quality and dose, image noise increases because the amount of X-rays in the energy range of the entire area is reduced. Thus, a newly improved non-local means (INLM) denoising algorithm was modeled and applied to X-ray images based on the thickness of the aluminum (Al) filter, which is most commonly used in X-ray imaging systems, to prove its potential use. For X-ray image acquisition, a high-performance cadmium telluride material-based detector and a tube containing Al filters (1-, 3-, and 5-mm thickness) were designed. The proposed INLM denoising algorithm was modeled by including the improved weights for the gradient information for each pixel in the conventional NLM-based equation. When the proposed INLM denoising algorithm was applied to the acquired photon-counting X-ray images, results showed superior performance for both noise characteristics and no-reference-based image evaluation. In particular, we confirmed that when the INLM was applied to X-ray images using 5 mm Al thickness, noise characteristics and no-reference-based evaluation results were improved by approximately 1.36 times and 1.06 times, respectively, compared to the conventional NLM. The study proved that choosing the INLM in photon-counting X-ray images using 5 mm-thick Al filtration is vital for the success of image processing applications.

关键词： Improved non-local means (INLM) approach denoising algorithm Photon-counting X-ray imaging Al filtration Quantitative evaluation of image quality

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A HYBRID IMAGE FUSION AND denoising algorithm BASED ON MULTI-SCALE TRANSFORMATION AND SIGNAL SPARSE REPRESENTATION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3500-3506页

作者： Sheng, Dajun Xinyang Univ Coll Big Data & Artificial Intelligence Xinyang 464000 Henan Peoples R China

In response to the problem of denoising in image fusion, the author proposes a hybrid image fusion and denoising algorithm based on multi-scale transformation (MLT) and signal sparse representation (SRS). A hybrid model is constructed for shear transformation, and the coefficients after MLT decomposition are thresholded. Sliding window technology and translation invariance are used to form sparse representation for image fusion, and SRS algorithm is used to remove noise from the source image. The experimental results show that the algorithm reduces the contrast and spectral information distortion of the fused image, displays high-quality visual fusion effects, maintains high PSNR values under different noise levels, can provide a more complete description of the features in the image, accurately judge the focus area, maintain the structural correlation of the image, and strengthen the description of fusion edges and details in the fused image. It has been proven that the methods of multi-scale transformation and sparse signal representation can fuse and denoise images.

关键词： Multiscale transformation Signal sparsity Image fusion denoising algorithm

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Predictive performance of denoising algorithms in S&P 500 and Bitcoin returns

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 260卷

作者： Gulay, Emrah Akgun, Omer Burak Bekiroglu, Korkut Duru, Okan Dokuz Eylul Univ Econometr Dept Izmir Turkiye Fibabanka R&D Ctr Istanbul Turkiye SharkNinja Ninja R&D Needham MA 02494 USA Ocean Dynamex Res Ottawa ON Canada

This paper aims to investigate the effectiveness of both the Hankel matrix and the Wavelet denoise methods to address the issue of denoising volatility in datasets, specifically squared returns, and to enhance the accuracy of post-sample forecasting. When converting data from levels to volatility, noise levels are amplified, and the forecasting models suffer from structural bias, leading to reduced accuracy gains. The main challenge in denoising operations is preserving systemic impulses while extracting sporadic and unpredictable components. While conventional denoising algorithms struggle to distinguish systemic patterns from noise, the Hankel matrix approach surpasses them in performance, resulting in improved predictive accuracy. Additionally, this paper explores the Wavelet denoise method, which is found to be less effective than the Hankel matrix approach for GARCH models. The empirical work showcases the denoising process and the accuracy improvements achieved in various datasets for GARCH models. By implementing the Hankel matrix approach, simulations reveal a notable accuracy gain of over 10%.

关键词： Volatility forecasting Data preprocessing denoising algorithm Hankel matrix Stock returns Cryptocurrency

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Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA)

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REMOTE SENSING 2025年第3期17卷 549-549页

作者： Hong, Yu Zhou, Runfa Liu, Jinfu Que, Xiang Chen, Bo Chen, Ke He, Zhongsheng Huang, Guanmin Fujian Agr & Forestry Univ Fuzhou 350002 Peoples R China Fujian Agr & Forestry Univ Key Lab Fujian Univ Ecol & Resource Stat Fuzhou 350002 Peoples R China Univ Idaho Dept Comp Sci Moscow ID 83844 USA Zhangjiangkou Natl Mangrove Nat Reserve Zhangzhou 363300 Peoples R China

Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising and spatiotemporal fusion, faced limitations: denoising algorithms usually enhance temporal resolution without improving spatial quality, while spatiotemporal fusion models struggle with prolonged data gaps and heavy noise. This study proposes an optimized mangrove phenology extraction approach (OMPEA), which integrates Landsat and MODIS data with a denoising algorithm (e.g., Gap Filling and Savitzky-Golay filtering, GF-SG) and a spatiotemporal fusion model (e.g., Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model, ESTARFM). The key of OMPEA is that GF-SG algorithm filled data gaps from cloud cover and satellite transit gaps, providing high-quality input to ESTARFM and improving its accuracy of NDVI imagery reconstruction in mangrove phenology extraction. By conducting experiments on the GEE platform, OMPEA generates 1-day, 30 m NDVI imagery, from which phenological parameters (i.e., the start (SoS), end (EoS), length (LoS), and peak (PoS) of the growing season) are derived using the maximum separation (MS) method. Validation in four mangrove areas along the coastal China shows that OMPEA significantly improves the potential to capture mangrove phenology in the presence of incomplete data. The OMPEA significantly increased usable data, adding 7-33 Landsat images and 318-415 MODIS images per region. The generated NDVI series exhibits strong spatiotemporal consistency with original data (R2: 0.788-0.998, RMSE: 0.007-0.253) and revealed earlier SoS and longer LoS at lower latitudes. Cross-correlation analysis showed a 2-3 month lagged effects of temperature on mangroves' growth, with precipitation having minimal impact. The proposed OMPEA improves the possibility of capturing mangrove phenology under non-continuous and low-resolution data, providing va

关键词： mangrove forests phenology monitoring OMPEA denoising algorithm spatiotemporal interpolation

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Anti-Fixed-Interference Fall Detection Based on Doppler-Time-Range Maps Using Millimeter-Wave Radar

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IEEE SENSORS JOURNAL 2025年第2期25卷 3358-3368页

作者： Ma, Zhanchao Wu, Xiaochuan Zhang, Xin Yao, Di Deng, Weibo Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China

With the growth of the number of elderly people, fall detection based on radio frequency signals for those vulnerable groups emerged. In radar-based fall detection methods, different heatmaps are generated from the echo signal. However, the noise and interference existing in the environment have a significant impact on heatmaps, which eventually lead to bad performances in fall detection. In this article, an anti-fixed-interference algorithm based on range-Doppler maps cancellation and a denoising algorithm based on Rayleigh probability distribution are proposed to ensure the consistency of the target's Doppler information in a Doppler-time (DT) map. To leverage the micro-Doppler information extracted from the movements of human limbs during falls, we construct a DT-range (DTR) map, which fuses 3-D information in a single map. Moreover, a neural network composed of 3-D convolutional neural networks and a bidirectional long short-term memory network is designed to extract 3-D features in DTR maps. The experiment is conducted in three different scenes, where a giant metal chassis, pipe vibration, and a fan are presented as an interference, respectively. Finally, based on the proposed methods, we achieve 96.16%, 94.21%, and 93.06% precision of fall detection in the above scenes, respectively.

关键词： Interference Fall detection Noise Three-dimensional displays Feature extraction Sensors Doppler effect Chirp Noise reduction Time-frequency analysis Anti-fixed-interference algorithm denoising algorithm doppler-time-range (DTR) map fall detection

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Ring artifact suppression and removal algorithm for an X-ray cone beam CT system

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2025年 1072卷

作者： Hang, Jiajuan Han, Jifeng Zhang, Yirong Yi, Chuqi Ma, Tianchi Long, Yiyang He, Junhan Wang, Penghui Qu, Guofeng Liu, Xingquan Sichuan Univ Inst Nucl Sci & Technol Key Lab Radiat Phys & Technol Minist Educ Chengdu 610064 Peoples R China

A cone-beam CT imaging system is designed to image internal structures of complex objects, and four analyticalbased algorithms are applied on projected domain sinograms to remove the ring artifacts, which are median filtering, Fourier transform, Fourier wavelet transform and sorting-based filter algorithm. The quantitative analysis of peak signal to noise ratio (PSNR) and the structural similarity index (SSIM) are carried to evaluate the performance of these algorithms. The results show that the universal methods that suitable to remove all artifacts of a complicated object doesn't exist. To address the issue, a slice classification method based on the internal structure of the object is proposed, and the most suitable denoising algorithm is identified for each category. The results demonstrate that this approach achieves optimal artifact elimination across all slices, thereby ensuring superior image quality.

关键词： X-ray CBCT denoising algorithm PSNR SSIM CNR COV

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An improved SVMD-based denoising method for micro thrust measurement signals

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第6期36卷

作者： Chen, Congyun Zhu, Chunyuan Kang, Dan Miao, Zhengqing Zhang, Shiyu Zhao, Meirong Zheng, Yelong Tianjin Univ State Key Lab Precis Measuring Technol & Instrumen Tianjin 300072 Peoples R China

Accurate thrust measurement is essential for spacecraft propulsion and space-based gravitational wave detection. However, thrust measurements are highly sensitive to environmental noise, making effective noise suppression critical for reliable signal acquisition. Thrust signals typically exhibit nonlinear abrupt transitions alongside smooth trends, posing challenges for traditional denoising methods, which often struggle to suppress noise while preserving key signal features. To address this issue, an improved successive variational mode decomposition (SVMD) algorithm optimized by the black-winged kite algorithm (BKA) is proposed. The BKA optimizes the penalty factor alpha based on a minimum envelope entropy fitness function. The enhanced SVMD then adaptively decomposes the signal, with the Hausdorff distance employed to distinguish between effective and noise modes, enabling accurate signal reconstruction. Experimental results on both simulated and actual thrust signals demonstrate that the proposed method significantly improves noise suppression compared to conventional approaches, while effectively preserving critical signal characteristics and enhancing data interpretation accuracy. These advantages make it a promising solution for thrust signal processing in high-precision measurement systems.

关键词： micro thrust measurement SVMD black-winged kite algorithm denoising algorithm

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Research on denoising algorithms and Corner Detection of Dangerous Chemicals Storage and Stacking Based on Generative Adversarial Networks 6

Research on Denoising Algorithms and Corner Detection of Dan...

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6th IEEE International Conference on Cloud Computing and Intelligence Systems (IEEE CCIS)

作者： Liu, Xuejun Wei, Yuchen Zhao, Zixian Qiao, Wen Lian, Yongning Li, Xintong Li, Cuiqing Beijing Inst Petrochem Technol Sch Informat Engn Beijing 102617 Peoples R China Beijing Inst Petrochem Technol Sch Chem Engn Beijing 102617 Peoples R China

ISBN: (纸本)9781728138633

Warehousing accidents of dangerous chemicals are destructive and harmful. The 'five distances' of palletizing in the warehouse is the key monitoring parameter. Binocular visual ranging is one of the technical means to solve this problem, but it is faced with the problem of serious shortage of data samples for storage and palletizing of dangerous chemicals. In this paper, the generative adversarial network (GAN) is used to extend the small sample data of palletizing in dangerous chemicals warehouse and apply to corner detection algorithm in ranging. The known palletizing samples are acquired by frame lifting of video stream. Based on the known samples, GAN algorithm is used to increase the number of samples stored in dangerous chemicals warehouse. The generated samples are extracted by edge features, corner detection and noise filtering (non-maximum suppression and kmeans clustering algorithm). Experiments show that 10,000 valid samples are generated by the generative adversarial network, and the generation speed is 8 to 10 times faster than that by the video stream frame lifting. The F-measure evaluation results of corner extraction algorithm show that the indexes of the generated samples are close to the real samples. The algorithm can provide strong technical support on sample for visual ranging in dangerous chemicals warehouse algorithm.

关键词： Warehousing of dangerous chemicals Generative adversarial networks Small sample data Corner detection denoising algorithm

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