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RetinexWT: Retinex-Based Low-Light Enhancement Method Combining Wavelet Transform 认领 引用
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作者 Hongji Chen Jianxun Zhang +2 位作者 Tianze Yu Yingzhu Zeng Huan Zeng 《Computers, Materials & Continua》 SCIE EI 2026年第2期2113-2132,共20页
Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional ... Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at http://gffzz188fe103f8f1460asx0vpufqwv0ub6fkv.ffgz.tsg.suse.edu.cn/CHEN-hJ516/RetinexWT(accessed on 14 October 2025). 展开更多
关键词 Low-light image enhancement retinex algorithm wavelet transform vision transformer
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Explainable electroencephalography-based attentiondeficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform 认领 引用
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作者 Yavuz Atas Serkan Kırık +9 位作者 Kübra Yıldırım Burak Tasci Prabal Datta Barua Ferhat Balgetir Sengul Dogan Turker Tuncer Ru-San Tan Elizabeth Palmer Aruna Devi U Rajendra Acharya 《World Journal of Psychiatry》 SCIE 2026年第3期141-158,共18页
BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is... BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is timeconsuming and subjective.Electroencephalography(EEG)signals provide an objective alternative,and machine learning methods can improve their diagnostic utility.AIM To develop an explainable EEG-based model for ADHD detection by integrating a novel combination ternary pattern(CTP)feature extractor with twin wavelet transform(TWT)for multilevel signal analysis,and to evaluate its effectiveness in providing accurate,channel-wise,and fusion-based classification results for objective and rapid ADHD diagnosis.METHODS A new EEG dataset containing more than 7000 segments from 137 ADHD patients and 150 controls was studied.A novel feature engineering framework was developed,combining a new CTP extractor with statistical features.A multilevel feature extraction structure was designed using a newly proposed TWT for signal decomposition.Extracted features were reduced to the most informative 263 using neighborhood component analysis.Channelwise classification was performed with k-nearest neighbors,followed by iterative majority voting across 20 EEG channels.RESULTS Single-channel analysis achieved up to 99.12%accuracy.By applying majority voting,overall classification accuracy increased to 99.97%,with similarly high sensitivity and specificity.CONCLUSION Our study introduces a large ADHD EEG dataset and a novel model integrating TWT and CTP.The model provides highly accurate,channel-wise,and fusion-based results,offering a promising objective tool for rapid ADHD diagnosis. 展开更多
关键词 Attention-deficit/hyperactivity disorder detection Combination ternary pattern Electroencephalography signal classification Explainable feature engineering Twin wavelet transform
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Block-Wise Sliding Recursive Wavelet Transform and Its Application in Real-Time Vehicle-Induced Signal Separation 认领 引用
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作者 Jie Li Nan An Youliang Ding 《Structural Durability & Health Monitoring》 EI 2026年第1期1-22,共22页
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ... Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring. 展开更多
关键词 Wavelet transform vehicle-induced signal separation real-time structure monitoring
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Prediction of wastewater treatment plant influent quality based on discrete wavelet transform and convolutional enhanced transformer 认领 引用
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作者 Lili Ma Danxia Li +2 位作者 Jinrong He Zhirui Niu Zhihua Feng 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第11期405-417,共13页
Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise ge... Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise generated from harsh operations and instruments,while the local feature pattern and long-term dependency in the wastewater quality time series,the prediction performance can be degraded.In this paper,a discrete wavelet transform and convolutional enhanced Transformer(DWT-Ce Transformer) method is developed to predict the influent quality in WWTPs.Specifically,we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform,effectively removing noise while preserving key data characteristics.Further,a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features,and then these local features are combined with Transformer's self-attention mechanism,so that the model can not only capture long-term dependencies,but also retain the sensitivity to local context.In this study,we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2.The experimental results show that,compared to baseline models,DWT-Ce Transformer can significantly improve the prediction performance of influent COD and NH3-N.Specifically,MSE,MAE,and RMSE improve by 78.7%,79.5%,and 53.8% for COD,and 79.4%,70.2%,and 54.5% for NH3-N.On simulated data,our method shows strong improvements under various weather conditions,especially in dry weather,with MSE,MAE,and RMSE for COD improving by 68.9%,48.0%,and 44.3%,and for NH3-N by 78.4%,54.8%,and 53.2%. 展开更多
关键词 Wastewater treatment plant Influent quality prediction Discrete wavelet transform Transformer Local feature Long-term dependencies
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Wavelet Transform Convolution and Transformer-Based Learning Approach for Wind Power Prediction in Extreme Scenarios 认领 引用
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作者 Jifeng Liang Qiang Wang +4 位作者 Leibao Wang Ziwei Zhang Yonghui Sun Hongzhu Tao Xiaofei Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期945-965,共21页
Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power gr... Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R2)as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions. 展开更多
关键词 Extreme scenarios conditional generative adversarial network wavelet transform Transformer wind power prediction
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Image Watermarking Algorithm Base on the Second Order Derivative and Discrete Wavelet Transform 认领 引用
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作者 Maazen Alsabaan Zaid Bin Faheem +1 位作者 Yuanyuan Zhu Jehad Ali 《Computers, Materials & Continua》 SCIE EI 2025年第7期491-512,共22页
Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embe... Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content.Image watermarking can also be used to verify the authenticity of digital media,such as images or videos,by ascertaining the watermark information.In this paper,a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform(DWT),chaotic map,and Laplacian operator.The DWT can be used to decompose the image into its frequency components,chaos is used to provide extra security defense by encrypting the watermark signal,and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the image.These mid-frequency bands are used to embed the watermarks by modifying the coefficients in these bands.The mid-sub-band maintains the invisible property of the watermark,and chaos combined with the second-order derivative Laplacian is vulnerable to attacks.Comprehensive experiments demonstrate that this approach is effective for common signal processing attacks,i.e.,compression,noise addition,and filtering.Moreover,this approach also maintains image quality through peak signal-to-noise ratio(PSNR)and structural similarity index metrics(SSIM).The highest achieved PSNR and SSIM values are 55.4 dB and 1.In the same way,normalized correlation(NC)values are almost 10%–20%higher than comparative research.These results support assistance in copyright protection in multimedia content. 展开更多
关键词 Discrete wavelet transform laplacian image watermarking chaos multimedia security
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Wavelet Transform-Based Bayesian Inference Learning with Conditional Variational Autoencoder for Mitigating Injection Attack in 6G Edge Network 认领 引用 被引量：1
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作者 Binu Sudhakaran Pillai Raghavendra Kulkarni +1 位作者 Venkata Satya Suresh kumar Kondeti Surendran Rajendran 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1141-1166,共26页
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies... Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies. 展开更多
关键词 Bayesian inference learning automaton convolutional wavelet transform conditional variational autoencoder malicious data injection attack edge environment 6G communication
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Drive-by damage detection and localization exploiting continuous wavelet transform and multiple sparse autoencoders 认领 引用
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作者 Lorenzo Bernardini Francesco Morgan Bono Andrea Collina 《Railway Engineering Science》 EI 2025年第4期721-745,共25页
Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners,in the attempt to make bridge condition-based monitoring more cost-efficient.In this work,the authors... Drive-by techniques for bridge health monitoring have drawn increasing attention from researchers and practitioners,in the attempt to make bridge condition-based monitoring more cost-efficient.In this work,the authors propose a drive-by approach that takes advantage from bogie vertical accelerations to assess bridge health status.To do so,continuous wavelet transform is combined with multiple sparse autoencoders that allow for damage detection and localization across bridge span.According to authors’best knowledge,this is the first case in which an unsupervised technique,which relies on the use of sparse autoencoders,is used to localize damages.The bridge considered in this work is a Warren steel truss bridge,whose finite element model is referred to an actual structure,belonging to the Italian railway line.To investigate damage detection and localization performances,different operational variables are accounted for:train weight,forward speed and track irregularity evolution in time.Two configurations for the virtual measuring channels were investigated:as a result,better performances were obtained by exploiting the vertical accelerations of both the bogies of the leading coach instead of using only one single acceleration signal. 展开更多
关键词 Drive-by Sparse autoencoder Steel truss railway bridge Continuous wavelet transform Damage detection Damage localization
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A Wavelet Transform and Spatial Positional Enhanced Method for Vision Transformer 认领 引用
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作者 HU Runyu TANG Xuesong HAO Kuangrong 《Journal of Donghua University(English Edition)》 CAS 2025年第3期330-338,共9页
In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the ... In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the capacity of ViT to capture global features,it compromises the preservation of fine-grained local feature information.To address these challenges,we propose a spatial positional enhancement module and a wavelet transform enhancement module tailored for ViT models.These modules aim to reduce spatial positional information loss during the patch embedding process and enhance the model’s feature extraction capabilities.The spatial positional enhancement module reinforces spatial information in sequential data through convolutional operations and multi-scale feature extraction.Meanwhile,the wavelet transform enhancement module utilizes the multi-scale analysis and frequency decomposition to improve the ViT’s understanding of global and local image structures.This enhancement also improves the ViT’s ability to process complex structures and intricate image details.Experiments on CIFAR-10,CIFAR-100 and ImageNet-1k datasets are done to compare the proposed method with advanced classification methods.The results show that the proposed model achieves a higher classification accuracy,confirming its effectiveness and competitive advantage. 展开更多
关键词 transformer wavelet transform image classification computer vision
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Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform 认领 引用
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作者 BALASUBRAMANIAN S NARUKA Mahaveer Singh TEWARI Gaurav 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第1期66-80,共15页
Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive met... Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising. 展开更多
关键词 electrocardiogram(ECG)signal denoising empirical wavelet transform(EWT) honey badge optimization(HBO) adaptive hybrid filter window function
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Undecimated Dual-Tree Complex Wavelet Transform and Fuzzy Clustering-Based Sonar Image Denoising Technique 认领 引用
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作者 LIU Biao LIU Guangyu +3 位作者 FENG Wei WANG Shuai ZHOU Bao ZHAO Enming 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期998-1008,共11页
Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do n... Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance. 展开更多
关键词 fuzzy clustering bilateral filtering undecimated dual-tree complex wavelet transform image denoising
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A Two-dimensional Genetic Algorithm Based on the Eno-Haar Wavelet Transform 认领 引用
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作者 宋锦萍 赵晨萍 李登峰 《Chinese Quarterly Journal of Mathematics》 北大核心 2007年第3期377-383,共7页
A two-dimensional genetic algorithm of wavelet coefficient is presented by using the ENO wavelet transform and the decomposed characterization of the two-dimensional Haar wavelet. And simulated by the ENO interpolatio... A two-dimensional genetic algorithm of wavelet coefficient is presented by using the ENO wavelet transform and the decomposed characterization of the two-dimensional Haar wavelet. And simulated by the ENO interpolation the article shows the affectivity and the superiority of this algorithm. 展开更多
关键词 wavelet transform ENO-Haar wavelet transform two-dimensional genetic algorithm
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Interpolation of Images Using Discrete Wavelet Transform to Simulate Image Resizing as in Human Vision 认领 引用 被引量：7
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作者 Rohini S. Asamwar Kishor M. Bhurchandi Abhay S. Gandhi 《International Journal of Automation and computing》 2010年第1期9-16,共8页
This paper presents discrete wavelet transform （DWT） and its inverse （IDWT） with Haar wavelets as tools to compute the variable size interpolated versions of an image at optimum computational load. As a human obse... This paper presents discrete wavelet transform （DWT） and its inverse （IDWT） with Haar wavelets as tools to compute the variable size interpolated versions of an image at optimum computational load. As a human observer moves closer to or farther from a scene, the retinal image of the scene zooms in or out, respectively. This zooming in or out can be modeled using variable scale interpolation. The paper proposes a novel way of applying DWT and IDWT in a piecewise manner by non-uniform down- or up-sampling of the images to achieve partially sampled versions of the images. The partially sampled versions are then aggregated to achieve the final variable scale interpolated images. The non-uniform down- or up-sampling here is a function of the required scale of interpolation. Appropriate zero padding is used to make the images suitable for the required non-uniform sampling and the subsequent interpolation to the required scale. The concept of zeroeth level DWT is introduced here, which works as the basis for interpolating the images to achieve bigger size than the original one. The main emphasis here is on the computation of variable size images at less computational load, without compromise of quality of images. The interpolated images to different sizes and the reconstructed images are benchmarked using the statistical parameters and visual comparison. It has been found that the proposed approach performs better as compared to bilinear and bicubic interpolation techniques. 展开更多
关键词 Discrete wavelet transform nomuniform sampling zeroeth level discrete wavelet transform （DWT） interpolation human vision.
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Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image 认领 引用 被引量：31
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作者 CHEN Bing-quan CUI Jin-ge +2 位作者 XU Qing SHU Ting LIU Hong-li 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第1期120-131,共12页
In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image,a denoising method of medical image based on discrete wavelet transform(DWT)and modified medi... In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image,a denoising method of medical image based on discrete wavelet transform(DWT)and modified median filter for medical image coupling denoising is proposed.The method is composed of four modules:image acquisition,image storage,image processing and image reconstruction.Image acquisition gets the medical image that contains Gaussian noise and impulse noise.Image storage includes the preservation of data and parameters of the original image and processed image.In the third module,the medical image is decomposed as four sub bands(LL,HL,LH,HH)by wavelet decomposition,where LL is low frequency,LH,HL,HH are respective for horizontal,vertical and in the diagonal line high frequency component.Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients,the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing.The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction.The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better,not a simple combination of the two previous methods.With DWT and improved median filter coefficients coupling denoising,it is highly practical for high-precision medical images containing complex noises.The experimental results of proposed algorithm are compared with the results of median filter,wavelet transform,contourlet and DT-CWT,etc.According to visual evaluation index PSNR and SNR and Canny edge detection,in low noise images,PSNR and SNR increase by 10%–15%;in high noise images,PSNR and SNR increase by 2%–6%.The experimental results of the proposed algorithm achieved better acceptable results compared with other methods,which provides an important method for the diagnosis of medical condition. 展开更多
关键词 medical image image denoising discrete wavelet transform modified median filter coupling denoising
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PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform 认领 引用 被引量：13
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作者 LIU Meijie DAI Yongshou +3 位作者 ZHANG Jie ZHANG Xi MENG Junmin XIE Qinchuan 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2015年第3期59-67,共9页
Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has b... Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification. 展开更多
关键词 sea ice optical remote sensing image SAR remote sensing image HIS transform wavelet transform PCA method
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Multi-Time Scale Analysis of Runoff at the Yangtze Estuary Based on the Morlet Wavelet Transform Method 认领 引用 被引量：12
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作者 KUANG Cui-ping SU Ping +4 位作者 GU Jie CHEN Wu-jun ZHANG Jian-le ZHANG Wan-lei ZHANG Yong-feng 《Journal of Mountain Science》 SCIE CSCD 2014年第6期1499-1506,共8页
Runoff series of the Yangtze River presents an intricate variation tendency under the reinforced influence of human activities.The Morlet Wavelet Transform method has been applied to analyze the annual runoff data fro... Runoff series of the Yangtze River presents an intricate variation tendency under the reinforced influence of human activities.The Morlet Wavelet Transform method has been applied to analyze the annual runoff data from 1950 to 2011 at the Yangtze River Estuary.It can clearly reveal the multi-time scales structure,break point,change and distribution of periodic variation in the different time scales of the runoff series.The main conclusions are that:1) Repeated periodic oscillations accompanied by an extremely large fluctuation are presented in the runoff series with an obvious difference between wet and dry years,and the major periods of the time series are about 3,8,16 and 23 years respectively.Among them,the presented maximum periodic oscillation is 23 years scale.2) In the 23-year time scale,the wet periods are 1950-1958,1969-1980 and 1992-2003,and the dry periods are 1959-1968,1981-1991 and 2004-2011.3) It can be predicted from the view of long time scales that the low annual runoff will likely occur in the near future. 展开更多
关键词 Datong station Wavelet transform Runoff series Periodic characteristics
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Measurement of interstation phase velocity by wavelet transformation 认领 引用 被引量：21
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作者 Qingju Wu Xiufen Zheng +2 位作者 Jiatie Pan Fengxue Zhang Guangcheng Zhang 《Earthquake Science》 2009年第4期425-429,共5页
In this paper,we present wavelet transformation method to measure interstation phase velocity.We use Morlet wavelet function as mother wavelet to filter two seismograms at various period of interest,and correlate the ... In this paper,we present wavelet transformation method to measure interstation phase velocity.We use Morlet wavelet function as mother wavelet to filter two seismograms at various period of interest,and correlate the wavelet filtered seismograms to form cross-correlogram.If both wavelet filtered signals are in phase at that period,the phase of the cross-correlogram is a minimum.Using 3-spline interpolation to transform cross-correlation matrix to a phase velocity verse period image,it is convenient for us to measure interstation phase velocity. 展开更多
关键词 dispesion phase velocity wavelet transform
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Multiple Time Scale Analysis of River Runoff Using Wavelet Transform for Dagujia River Basin, Yantai, China 认领 引用 被引量：10
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作者 LIU Delin LILT Xianzhao +2 位作者 LI Bicheng ZHAO Shiwei LI Xiguo 《Chinese Geographical Science》 SCIE 2009年第2期158-167,共10页
Based on monthly river runoff and meteorological data, a method of Morlet wavelet transform was used to analyze the multiple time scale characteristics of river runoff in the Dagujia River Basin, Yantai City, Shandong... Based on monthly river runoff and meteorological data, a method of Morlet wavelet transform was used to analyze the multiple time scale characteristics of river runoff in the Dagujia River Basin, Yantai City, Shandong Province. The results showed that the total annual river runoffin the Dagujia River Basin decreased significantly from 1966 to 2004, and the rate of decrease was 48× 10^6ma/10yr, which was higher than the mean value of most rivers in China. Multiple time scale characteristics existed, which accounted for different aspects of the changes in annual river runoff, and the major periods of the runofftime series were identified as about 28 years, 14 years and 4 years with decreasing levels of fluctuation. The river runoff evolution process was controlled by changes in precipitation to a certain extent, but it was also greatly influenced by human activities. Also, for different time periods and scales, the impacts of climate changes and human activities on annual river runoff evolution occurred at the same time. Changes in the annual river runoffwere mainly associated with climate change before the 1980s and with human activities after 1981. 展开更多
关键词 multiple time scale river runoff climate change Morlet wavelet transform Dagujia River
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Gravity compression forward modeling and multiscale inversion based on wavelet transform 认领 引用 被引量：6
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作者 Sun Si-Yuan Yin Chang-Chun +2 位作者 Gao Xiu-He Liu Yun-He Ren Xiu-Yan 《Applied Geophysics》 SCIE CSCD 2018年第2期342-352,365,共11页
The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting me... The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method. 展开更多
关键词 Wavelet transform matrix compression multiscale inversion gravity forwardmodeling
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A study of wavelet transforms applied for fracture identification and fracture density evaluation 认领 引用 被引量：8
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作者 张晓峰 潘保芝 +1 位作者 王飞 韩雪 《Applied Geophysics》 SCIE CSCD 2011年第2期164-169,178,179,共6页
Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wave... Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet.Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve,identifying the fractured zone.Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density.This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent. 展开更多
关键词 Wavelet transform fracture identification differential curves fracture density
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