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curvelet transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Computers, Materials & Continua 2022年第5期71卷 2459-2476页

作者： Sonali Dash Sahil Verma Kavita N.Z.Jhanjhi Mehedi Masud Mohammed Baz Department of Electronics and Communication Engineering Raghu Institute of Technology(A)Visakhapatnam531162India Department of Computer Science and Engineering Chandigarh UniversityMohali140413India School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye *** diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular *** various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s *** curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge *** bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong *** bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the ***,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the *** C mean thresholding is used for the extraction of *** recommended fusion approach is assessed on DRIVE *** results illustrate that the fusion algorithm preserved the advantages of the both and provides better *** results demonstrate that the recommended method outperforms the traditional approaches.

关键词： Blood vessel extraction curvelet transform fast bilateral filter C mean thresholding

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curvelet transform-Based Denoising Method for Doppler Frequency Extraction

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Journal of Beijing Institute of Technology 2007年第4期16卷 455-459页

作者：侯舒娟吴嗣亮 School of Information Science and Technology Beijing Institute of Technology

A novel image denoising method based on curvelet transform is proposed in order to improve the performance of Doppler frequency extraction in low signal-noise-ratio (SNR) environment. The echo can be represented as a gray image with spectral intensity as its gray values by time-frequency transform. And the curvelet coefficients of the image are computed. Then an adaptive soft-threshold scheme based on dual-median operation is implemented in curvelet domain. After that, the image is reconstructed by inverse curvelet transform and the Doppler curve is extracted by a curve detection scheme. Experimental results show the proposed method can improve the detection of Doppler frequency in low SNR environment.

关键词： miss distance Doppler frequency curvelet transform denoising

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Surface Detection of Continuous Casting Slabs Based on curvelet transform and Kernel Locality Preserving Projections

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Journal of Iron and Steel Research International 2013年第5期20卷 80-86页

作者： AI Yong-hao XU Ke National Engineering Research Center for Advanced Rolling Technology University of Science and Technology Beijing

Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recognition of cracks is essential because the surface of hot slabs is very complicated. In order to detect the surface longitudinal cracks of the slabs, a new feature extraction method based on curvelet transform and kernel locality preserving projections （KLPP） is proposed. First, sample images are decomposed into three levels by curvelet transform. Second, Fourier transform is applied to all sub-band images and the Fourier amplitude spectrum of each sub-band is computed to get features with translational invariance. Third, five kinds of statistical features of the Fourier amplitude spectrum are computed and combined in different forms. Then, KLPP is employed for dimensionality reduction of the obtained 62 types of high-dimensional combined features. Finally, a support vector machine （SVM） is used for sample set classification. Experiments with samples from a real production line of continuous casting slabs show that the algorithm is effective to detect longitudinal cracks, and the classification rate is 91.89%.

关键词： surface detection continuous casting slab curvelet transform feature extraction kernel locality preserving projections

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Extraction of reflected waves from acoustic logging data using variation mode decomposition and curvelet transform

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Petroleum Science 2024年第5期21卷 3142-3156页

作者： Fan-Tong Kong Yong-Xiang Liu Xi-Hao Gu Li Zhen Cheng-Ming Luo Sheng-Qing Li Ocean College Jiangsu University of Science and TechnologyZhenjiang212100jiangsuChina School of Geosciences China University of Petroleum(East China)Qingdao266580Shandong.China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Chengdu University of TechnologyChengdu610059SichuanChina

Remote reflection waves, essential for acquiring high-resolution images of geological structures beyond boreholes, often suffer contamination from strong direct mode waves propagating along the ***, the extraction of weak reflected waves becomes pivotal for optimizing migration image quality. This paper introduces a novel approach to extracting reflected waves by sequentially operating in the spatial frequency and curvelet domains. Using variation mode decomposition(VMD), single-channel spatial domain signals within the common offset gather are iteratively decomposed into high-wavenumber and low-wavenumber intrinsic mode functions(IMFs). The low-wavenumber IMF is then subtracted from the overall waveform to attenuate direct mode waves. Subsequently, the curvelet transform is employed to segregate upgoing and downgoing reflected waves within the filtered curvelet domain. As a result, direct mode waves are substantially suppressed, while the integrity of reflected waves is fully preserved. The efficacy of this approach is validated through processing synthetic and field data, underscoring its potential as a robust extraction technique.

关键词： Borehole acoustic reflection imaging Variation mode decomposition curvelet transform Weak signal extraction

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Speckle suppression in synthetic aperture radar ocean internal solitary wave images with curvelet transform

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Acta Oceanologica Sinica 2016年第9期35卷 13-21页

作者： ZHA Guozhena HE Qingyou GUAN Changlonga SUN Jian HE Mingxia College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China University of Chinese Academy of Sciences Beijing 100049China Physical Oceanography Laboratory Ocean University of ChinaQingdao 266100China Ocean Remote Sensing Institute Ocean University of ChinaQingdao 266100China

This paper proposes a speckle-suppression method for ocean internal solitary wave（ISW） synthetic aperture radar（SAR） images by using the curvelet *** band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional *** curvelet transform possesses a very high scale and directional ***,the curvelet transform is very efficient in analyzing wave signals in SAR images.A noisy ocean ISW SAR image can be decomposed at different scales,directions,and positions using the curvelet *** information of the ISWs is centralized in the curvelet coefficients of certain directions under certain scales,whereas the speckle noise is distributed in every scale and *** manipulating the curvelet coefficients,the signals of the ISWs can be extracted from the noisy SAR ***,the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR *** demonstrate the effectiveness of this method.

关键词： curvelet transform internal solitary wave remote sensing speckle noise synthetic aperture radar

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Junk band recovery for hyperspectral image based on curvelet transform

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Journal of Central South University 2011年第3期18卷 816-822页

作者：孙蕾罗建书 School of Sciences National University of Defe nse and Technology

Under consideration that the profiles of bands at close wavelengths are quite similar and the curvelets are good at capturing profiles, a junk band recovery algorithm for hyperspectral data based on curvelet transform is proposed. Both the noisy bands and the noise-free bands are transformed via curvelet band by band. The high frequency coefficients in junk bands are replaced with linear interpolation of the high frequency coefficients in noise-flee bands, and the low frequency coefficients remain the same to keep the main spectral characteristics from being distorted. Jutak bands then are recovered after the inverse curvelet transform. The performance of this method is tested on the hyperspectral data cube obtained by airborne visible/infrared imaging spectrometer （AVIRIS）. The experimental results show that the proposed method is superior to the traditional denoising method BayesShrink and the art-of-state curvelet Shrinkage in both roots of mean square error （RMSE） and peak-signal-to-noise ratio （PSNR） of recovered bands.

关键词： hyperspectral image curvelet transform junk band denosing

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Super-Resolution Based on curvelet transform and Sparse Representation

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Computer Systems Science & Engineering 2023年第4期45卷 167-181页

作者： Israa Ismail Mohamed Meselhy Eltoukhy Ghada Eltaweel Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology University of JeddahCollege of Computing and Information Technology at KhulaisJeddahSaudi Arabia

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution *** uses the nonlocal mean filter as a prior step to produce a denoised *** proposed algorithm is based on curvelet *** converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both *** parallel,we applied sparse representation with over complete dictionary for the denoised *** proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher *** experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced *** comparison study shows that the proposed super-resolution algorithm outperforms the *** mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.

关键词： Super-resolution curvelet transform non-local means filter lancozos interpolation sparse representation

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A 4-quadrant curvelet transform for Denoising Digital Images

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International Journal of Automation and computing 2013年第3期10卷 217-226页

作者： P. K. Parlewar K. M. Bhurchandi Department of Electronic and Communication Engineering Ramdeobaba College of Engineering and Management Department of Electronic Engineering Visvesvaraya National Institute of Technology

The conventional discrete wavelet transform （DWT） introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform （FRIT） solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform （FCT） selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform （4QFCT） based on a new concept of 4-quadrant finite ridgelet transform （4QFRIT） has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error （MSE） and peak signal to noise ratio （PSNR）. The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.

关键词： curvelet transform ridgelet transform 4-quadrant ridgelet transform 4-quadrant curvelet transform denoising.

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An Optimised Defensive Technique to Recognize Adversarial Iris Images Using curvelet transform

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Intelligent Automation & Soft Computing 2023年第1期35卷 627-643页

作者： K.Meenakshi G.Maragatham School of Computing Department of Networking and CommunicationsSRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computing Department of Computational IntelligenceSRM Institute of Science and TechnologyKattankulathurChennai603203India

Deep Learning is one of the most popular computer science techniques,with applications in natural language processing,image processing,pattern iden-tification,and various *** the success of these deep learning algorithms in multiple scenarios,such as spam detection,malware detection,object detection and tracking,face recognition,and automatic driving,these algo-rithms and their associated training data are rather vulnerable to numerous security *** threats ultimately result in significant performance ***,the supervised based learning models are affected by manipulated data known as adversarial examples,which are images with a particular level of noise that is invisible to *** inputs are introduced to purposefully confuse a neural network,restricting its use in sensitive application areas such as bio-metrics *** this paper,an optimized defending approach is proposed to recognize the adversarial iris examples *** curvelet transform Denoising method is used in this defense strategy,which examines every sub-band of the adversarial images and reproduces the image that has been changed by the *** salient iris features are retrieved from the reconstructed iris image by using a pretrained Convolutional Neural Network model(VGG 16)followed by Multiclass *** classification is performed by using Support Vector Machine(SVM)which uses Particle Swarm Optimization method(PSO-SVM).The proposed system is tested when classifying the adversarial iris images affected by various adversarial attacks such as FGSM,iGSM,and Deep-fool *** experimental result on benchmark iris dataset,namely IITD,produces excellent outcomes with the highest accuracy of 95.8%on average.

关键词： Adversarial attacks biometrics curvelet transform CNN particle swarm optimization adversarial iris recognition

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GPR data noise attenuation on the curvelet transform

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Applied Geophysics 2014年第3期11卷 301-310,351页

作者：包乾宗李庆春陈文超长安大学地质工程与测绘学院西安710054 西安交通大学电子与信息工程学院波动与信息研究所西安710049

Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to fi lter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.

关键词： Signal extraction background noise curvelet transform threshold value noise attenuation

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