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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 diseases.Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure.Although various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s edges.The curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge enhancement.The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges.Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges.Therefore,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 image.Afterwards C mean thresholding is used for the extraction of vessel.The recommended fusion approach is assessed on DRIVE dataset.Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result.The 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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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 algorithm.It uses the nonlocal mean filter as a prior step to produce a denoised image.The proposed algorithm is based on curvelet transform.It converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands.In parallel,we applied sparse representation with over complete dictionary for the denoised image.The 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 resolution.The experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced edges.The comparison study shows that the proposed super-resolution algorithm outperforms the state-of-the-art.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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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 otherfields.Despite 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.These threats ultimately result in significant performance degradation.Moreover,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 humans.Adversarial inputs are introduced to purposefully confuse a neural network,restricting its use in sensitive application areas such as bio-metrics applications.In this paper,an optimized defending approach is proposed to recognize the adversarial iris examples efficiently.The 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 attacker.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.The 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 methods.An 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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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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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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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 transform.The band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional characteristics.The curvelet transform possesses a very high scale and directional sensitivity.Therefore,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 transform.The 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 direction.By manipulating the curvelet coefficients,the signals of the ISWs can be extracted from the noisy SAR image.Finally,the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR image.Experiments 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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Oil Spill Identification in SAR Image Using curvelet transform and SVM

Oil Spill Identification in SAR Image Using Curvelet Transfo...

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作者： ZHOU Hui CHEN Peng College of Computer and Software Dalian Neusoft Information University Navigation College Dalian Maritime University

at present,the problem of marine pollution caused by oil spill accidents is increasingly serious.Rapid and accurate automatic recognition of SAR images provides an important prerequisite for the handling and decision of oil spill accidents.This paper proposes a feature extraction method for SAR images based on curvelet transform.First,we performed discrete curvelet transform and selected the low-frequency component as a new image matrix,which contains the main information.Then,the Principal Component Analysis（PCA）technique was applied to select the best features to reduce the dimension.Finally,the Support Vector Machine（SVM）classifier was used to distinguish between "oil slicks" and "look-alikes oil slicks" and verify the validity of the extracted features.Experiments were performed on the different datasets,and the results proved that the accuracy of recognition is improved with curvelet transform.In addition,compared with other neural network algorithms,curvelet transform is an effective way to extract a reduced set of discriminative features for SAR images.

关键词： curvelet transform Feature extraction SAR image recognition

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Diffraction separation and imaging based on double sparse transforms

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Petroleum Science 2022年第2期19卷 534-542页

作者： Xue Chen Jing-Jie Cao He-Long Yang Shao-Jian Shi Yong-Shuai Guo Hebei Key Laboratory of Strategic Critical Mineral Resources Hebei GEO UniversityShijiazhuangHebei 050031China Hebei Nautral Resources Archives ShijiazhuangHebei 050037China The Third Hydrogeological Engineering Geological Brigade HengshuiHebei 053000China

Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fractures,etc.Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging.In the common-offset domain,reflections are mostly expressed as smooth linear events,whereas diffractions are characterized by hyperbolic events.This paper proposes a diffraction extraction method based on double sparse transforms.The linear events can be sparsely expressed by the high-resolution linear Radon transform,and the curved events can be sparsely expressed by the curvelet transform.A sparse inversion model is built and the alternating direction method is used to solve the inversion model.Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.

关键词： Diffraction separation Common-offset domain Diffraction imaging High-resolution linear Radon transform curvelet transform Sparse inversion

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A Spatially Adaptive Denoising Algorithm Based on curvelet transform

A Spatially Adaptive Denoising Algorithm Based on Curvelet T...

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2012年计算机应用与系统建模国际会议

作者： Peng Feng Feng Yang Biao Wei Deling Mi Key Laboratory of Opto-electronics Technology & System Ministry of Education Chongqing University

A new approach for image denoising based on the curvelet transform is presented in this paper. The existing theory for curvelet and Ridgelet suggests that these new approaches can outperform wavelet method in certain image processing including image denoising, edge detection and image enhancement. However in original digital curvelet transform it uses a simple hard-thresholding rule for filtering the noisy curvelet coefficient which of course causes some problems such as killing too many signal curvelet coefficients that might contain useful image information. Here we introduce BayesShrink denoising scheme into curvelet domain that is an adaptive, data-driven thresholding approach for image denoising, namely CurShrink. The threshold is derived in a Bayesian framework, and the prior used on the curvelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The approach is valid because curvelet transform produce correlated curvelet coefficients, along the edge or curve of the image;the large curvelet coefficients maybe have large curvelet coefficients as it neighbors. Experimental results show that the proposed method is better than hard-thresholding denoising scheme in wavelet and curvelet domain.

关键词： curvelet transform image denoising Multi-scale Geometric Analysis（MGA） CurShrink

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Huber inversion-based reverse-time migration with de-primary imaging condition and curvelet-domain sparse constraint

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Petroleum Science 2022年第4期19卷 1542-1554页

作者： Bo Wu Gang Yao Jing-Jie Cao Di Wu Xiang Li Neng-Chao Liu State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing102249China Unconventional Petroleum Research Institute China University of Petroleum(Beijing)Beijing102249China Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration Ministry of Natural ResourcesHebei GEO University050031China Hebei Key Laboratory of Strategic Critical Mineral Resources Hebei GEO UniversityShijiazhuangHebei050031China College of Geophysics China University of Petroleum(Beijing)Beijing102249China

Least-squares reverse-time migration(LSRTM) formulates reverse-time migration(RTM) in the leastsquares inversion framework to obtain the optimal reflectivity image. It can generate images with more accurate amplitudes, higher resolution, and fewer artifacts than RTM. However, three problems still exist:(1) inversion can be dominated by strong events in the residual;(2) low-wavenumber artifacts in the gradient affect convergence speed and imaging results;(3) high-wavenumber noise is also amplified as iteration increases. To solve these three problems, we have improved LSRTM: firstly, we use Hubernorm as the objective function to emphasize the weak reflectors during the inversion;secondly, we adapt the de-primary imaging condition to remove the low-wavenumber artifacts above strong reflectors as well as the false high-wavenumber reflectors in the gradient;thirdly, we apply the L1-norm sparse constraint in the curvelet-domain as the regularization term to suppress the high-wavenumber migration noise. As the new inversion objective function contains the non-smooth L1-norm, we use a modified iterative soft thresholding(IST) method to update along the Polak-Ribie re conjugate-gradient direction by using a preconditioned non-linear conjugate-gradient(PNCG) method. The numerical examples,especially the Sigsbee2 A model, demonstrate that the Huber inversion-based RTM can generate highquality images by mitigating migration artifacts and improving the contribution of weak reflection events.

关键词： Least-squares reverse-time migration Huber-norm Sparse constraint curvelet transform Iterative soft thresholding

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