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Olive Leaf Disease Detection via wavelet transform and Feature Fusion of Pre-Trained Deep Learning Models

Computers, Materials & Continua 2024年第3期78卷 3431-3448页

作者： Mahmood A.Mahmood Khalaf Alsalem Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakahKingdom of Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityGizaEgypt

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关键词： Olive leaf diseases wavelet transform deep learning feature fusion

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WavEnhancer: Unifying wavelet and transformer for Image Enhancement

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Journal of Computer Science & Technology 2024年第2期39卷 336-345页

作者：李梓诺陈绪行郭淑娜王书强潘治文 Department of Computer and Information Science University of MacaoMacao 999078China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.

关键词： transformer wavelet transform image enhancement

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Dual-stream coupling network with wavelet transform for cross-resolution person re-identification

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Journal of Systems Engineering and Electronics 2023年第3期34卷 682-695页

作者： SUN Rui YANG Zi ZHAO Zhenghui ZHANG Xudong Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education) Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China

Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.

关键词： cross-resolution feature invariant learning person re-identification residual knowledge transfer wavelet transform

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Are population cycles recovering?

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Integrative Zoology 2024年第3期19卷 538-547页

作者： Vasily D.YAKUSHOV Boris I.SHEFTEL A.N.Severtsov Institute of Ecology and Evolution of the Russian Academy of Sciences MoscowRussia

The dynamics of populations of small mammals of Central Siberia was analyzed.The studies were carried out at the Yenisei ecological station“Mirnoye”of the A.N.Severtsov Institute of Ecology and Evolution,Russian Academy of Sciences.The time series analysis was performed by the wavelet transform using the statistical data processing language R.In the 20th century,the dynamics of the population of the community and some of its constituent species(Sorex araneus;S.caecutiens;S.isodon;S.tundrensis;S.minutus;Craseomys rufocanus;Clethrionomys rutilus;Microtus oeconomus;M.agrestis)were characterized by a 4-year periodicity.The type of dynamics changed to noncyclic by the nineties,but by 2022,four species(S.araneus,S.isodon,C.rutilus,and M.oeconomus)and the community as a whole showed a tendency toward recovery of population cycles.The remaining species were characterized by consistently low numbers with irregular low amplitude fluctuations.

关键词： population cycles small mammals time-series analysis wavelet transform

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions.Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations.Deep learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow.However,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images.This study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction.In the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial characteristics.Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics.These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced predictions.Experimental results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,respectively.We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow.SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings.The enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind comfort.The proposed spatial-frequency loss term is general and can be flexibly inte

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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wavelet transform for Fresnel-transformed mother wavelets

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Chinese Physics B 2011年第12期20卷 57-62页

作者：刘述光陈俊华范洪义 School of Mathematics and Physics Anhui Polytechnic University Department of Materials Science and Engineering University of Science and Technology of China

In this paper, we propose the so-called continuous Fresnel-wavelet combinatorial transform which means that the mother wavelet undergoes the Fresnel transformation. This motivation can let the mother-wavelet-state itself vary from ｜ψ〉 to Ftr, s ｜ψ〉, except for variation within the family of dilations and translations. The Parseval＇s equality, admissibility condition and inverse transform of this continuous Fresnel-wavelet combinatorial transform are analysed. By taking certain parameters and using the admissibility condition of this continuous Fresnel-wavelet combinatorial transform, we obtain some mother wavelets. A comparison between the newly found mother wavelets is presented.

关键词： wavelet transform fresnel transformation quantum state vector

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THE wavelet transform OF PERIODIC FUNCTION AND NONSTATIONARY PERIODIC FUNCTION

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Applied Mathematics and Mechanics(English Edition) 2002年第9期23卷 1062-1070页

作者：刘海峰周炜星王辅臣龚欣于遵宏 College of Resource and Environmental Engineering East China University of Science and TechnologyShanghai 200237P R China

Some properties of the wavelet transform of trigonometric Junction, periodic function and nonstationary periodic function have been investigated. The results show that the peak height and width in wavelet energy spectrum of a periodic function are in proportion to its period. At the same time, a new equation, which can truly reconstruct a trigonometric function with only one scale wavelet coefficient, is presented. The reconstructed wave shape of a periodic function with the equation is better than any term of its Fourier series. And the reconstructed wave shape of a class of nonstationary periodic function with this equation agrees well with the function.

关键词： wavelet transform periodic function nonstationary periodic function Fourier transform

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wavelet transform and Radon transform on the Quaternion Heisenberg Group

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Acta Mathematica Sinica,English Series 2014年第4期30卷 619-636页

作者： Jian Xun HE He Ping LIU School of Mathematics and Information Sciences Guangzhou University Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education Institutes Guangzhou University LMAM School of Mathematical SciencesPeking University

Let be the quaternion Heisenberg group, and let P be the affine automorphism group of . We develop the theory of continuous wavelet transform on the quaternion Heisenberg group via the unitary representations of P on L2（）. A class of radial wavelets is constructed. The inverse wavelet transform is simplified by using radial wavelets. Then we investigate the Radon transform on . . A Semyanistyi-Lizorkin space is introduced, on which the Radon transform is a bijection. We deal with the Radon transform on both by the Euclidean Fourier transform and the group Fourier transform. These two treatments are essentially equivalent. We also give an inversion formula by using wavelets, which does not require the smoothness of functions if the wavelet is smooth. In addition, we obtain an inversion formula of the Radon transform associated with the sub-Laplacian on .

关键词： Quaternion Heisenberg group wavelet transform Radon transform inverse Radon transform

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wavelet transform-Based High-Definition Map Construction From a Panoramic Camera

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 569-576页

作者：庄瀚洋周学军王春香钱宇晗 University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China Guangxi Key Laboratory of Automobile Components and Vehicle Technology Guangxi University of Science and TechnologyLiuzhou 545006GuangxiChina Noblelift Intelligent Equipment Co. Ltd.Huzhou 313100ZhejiangChina Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

High-definition(HD)maps are key components that provide rich topologic and semantic information for decision-making in vehicle autonomous driving systems.A complete ground orthophoto is usually used as the base image to construct the HD map.The ground orthophoto is obtained through inverse perspective transformation and image mosaicing.During the image mosaicing,multiple consecutive orthophotos are stitched together using pose information and image registration.In this study,wavelet transform is introduced to the image mosaicing process to alleviate the information loss caused by image overlapping.In the orthophoto wavelet transform,high-frequency and low-frequency components are fused using different strategies to form a complete base image with clearer local details.Experimental results show that the accuracy of the orthophotos generated using this method is improved.

关键词： high-definition(HD)map wavelet transform image registration panorama

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wavelet transform Approach to Segment Thermal Image

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Journal of Beijing Institute of Technology 2003年第S1期12卷 33-38页

作者：付梦印张长江李杰金梅 Department of Automatic Control School of Information and Science TechnologyBeijing Institute of Technology Beijing 100081China School of Electrics and Information Engineering Daqing Petroleum InstituteDaqingHeilongiang 163318.China

An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histogram is smoothed by Bezier curve and Bezier histogram is obtained. One dimension stationary wavelet transform is done to the curvature curve of Bezier histogram. Positions of peak values of curvature curve in wavelet domain are adjusted from 'fine-to-coarse' at all scales. The gray level values, which are located in adjusted peak values at all scales, are considered as segmentation thresholds. The gray level values of valley between peaks are considered as quantity gray levels. Optimal segmentation scale is obtained by a cost criterion. The results of experiment show that a target can be segmented effectively from complex background in thermal image by new approach.

关键词： wavelet transform thermal image segmentation Bezier histogram curvature

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