An Optimised Defensive Technique to Recognize Adversarial Iris Images Using Curvelet Transform

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.

Super-Resolution Based on Curvelet Transform and Sparse Representation

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.

Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and Arithmetic Coding

Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

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

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.

Curvelet Transform-Based Denoising Method for Doppler Frequency Extraction

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.

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

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.

Digital Image Watermarking Algorithm Based on Fast Curvelet Transform

A digital image watermarking algorithm based on fast curvelet transform is proposed. Firstly, the carrier image is decomposed by fast curvelet transform, and, the watermarking image is scrambled by Arnold transform. Secondly, the binary watermarking image is embedded into the medium frequency coefficients according to the human visual characteristics and curvelet coefficients. Experiment results show that the proposed algorithm has good performance in both invisibility and security and also has good robustness against the noise, cropping, filtering, JPEG compression and other attacks.

Weak Seismic Signal Extraction Based on the Curvelet Transform

Seismic signal denoising is a key step in seismic data processing.Airgun signals are easy to be interfered with by noise when it travels a long distance due to the weak energy of active source signal of the airgun.Aiming to solve this problem,and considering that the conventional Curvelet transform threshold processing method does not use the seismic spectrum information,we independently process the Curvelet scale layer corresponding to valid data based on the characteristics of the Curvelet transform of multi-scale,multi-direction and capable of expressing the sparse seismic signals in order to fully excavate the information features.Combined with the Curvelet adaptive threshold denoising the algorithm,we apply the Curvelet transform to denoising seismic signals while retaining the weak information in the signal as much as possible.The simulation experiments show that the improved threshold denoising method based on Curvelet transform is superior to the frequency domain filtering,wavelet denoising and traditional Curvelet denoising method in detailed information extraction and signal denoising of low SNR signals.The calculation accuracy of the relative wave velocity variation of underground medium is improved.

Junk band recovery for hyperspectral image based on curvelet transform

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-free bands,and the low frequency coefficients remain the same to keep the main spectral characteristics from being distorted.Junk 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.

基于广义Curvelet变换和相空间特征聚类的VSP波场分离方法

垂直地震剖面(Vertical Seismic Profiling,VSP)蕴含大量的地层地质信息,是连接地震反射数据与测井资料之间的桥梁.VSP数据分辨率高,资料丰富,包括上行波、下行波、转换波等多种类型的波场,常被用于地层反射界面标定、介质衰减参数反演等.其中,如何有效地分离波场是将VSP资料应用于油气勘探的关键之一.广义Curvelet变换是一种具有多尺度、多角度的相空间变换,能够进行相空间域波场特性的提取.本文聚焦于VSP波场的上下行波分离问题,提出一种基于广义Curvelet变换和相空间特征聚类方法.首先,采用广义Curvelet变换对VSP波场进行特征提取,构造融入相空间角度信息的特征数据集;然后,利用K-means聚类算法在相空间对波场特征进行分类;最后,对分类结果进行反变换,完成VSP上下行波波场的自适应分离.为了验证方法的有效性,本文将所提出的方法用于合成数据和实际VSP数据的波场分离,并与常用的基于F-K变换的波场分离方法进行对比.处理结果表明,本文方法角度分辨率高、抗噪能力强,波场分离结果的保真保幅性好,这为后续的成像与地层的特征分析提供了重要基础资料.

基于先验信息约束的Curvelet域地震数据POCS插值方法

由于野外采集环境的限制,常常无法采集得到完整规则的野外地震数据,为后续地震处理、解释工作的顺利进行,需要进行地震数据重构。凸集投影(POCS)方法利用地震波形在Curvelet域的稀疏特性,可以重构出高信噪比地震数据,该迭代算法稳定,其收敛速度较快。但在地震数据恢复的时候,由于直达波和炮集上部空白区域的影响,随着迭代的进行,重构数据中噪声干扰越来越严重,导致最终恢复的地震数据信噪比较低。本文在实现POCS迭代阈值算法基础上,引入先验信息约束的思想对算法进行优化。通过先进行坐标映射的方法进行炮集插值,然后将其作为先验信息约束进行插值,可以有效地压制迭代噪音对重构地震波形数据的影响。通过合成地震炮记录与实际炮集进行测试,结果表明本文提出的改进方法可以明显改善重构地震数据的信噪比,并提高地震波场同相轴的连续性。

A 4-quadrant Curvelet Transform for Denoising Digital Images

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.

一种基于Curvelet变换的地震数据去噪方法和应用

如何去除噪声且不损失有效信号是地震数据处理中的一个重难点。实验将一种Curvelet自适应阈值去噪法应用到地震数据处理中,Curvelet变换系数由自适应阈值约束,经阈值函数映射得到估计系数,最后对所得的估计系数进行逆变换,实现地震数据去噪,有效提高地震数据信噪比。模型和实际数据处理结果表明,Curvelet自适应阈值去噪法较好地压制了噪声的同时保护有效信号,克服了F-X域滤波产生新噪声的缺陷,取得了较好去噪效果。

Surface Detection of Continuous Casting Slabs Based on Curvelet Transform and Kernel Locality Preserving Projections

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%.

基于Fhyp-curvelet钢筋抑制的灾害目标成像方法

为了提高路面的强度和耐用性,一般会在路面下进行钢筋加固,钢筋回波与待检测的灾害目标回波在时空域上可能会产生严重的混叠。为了对灾害目标进行有效的检测与成像,文中提出一种钢筋回波干扰下的灾害目标成像方法。首先,利用快速双曲线曲波变换将雷达接收信号变换到多个不同的尺度空间,在该空间中所有目标的能量是聚集的且相互分离;然后,进行钢筋回波滤波,将灾害目标的回波数据重构到时空域后再变换到波数域,在该域下考虑多层地下介质中电磁波波速变化的特点;最后,再反变换到时空域进行灾害目标成像。文中所提成像方法均采用快速处理算法,提高了运算速度,且适用于多层地下介质中钢筋回波干扰下的灾害目标成像。仿真结果证明了本文所提算法的有效性。

Curvelet transform and contrast adaptive clip histogram equalization-based image defogging algorithm

Due to the scattering effect of suspended particles in the atmosphere, foggy day images have reduced visibility and contrast significantly. Considering the loss of details and uneven defogging results of the contrast limited adaptive histogram equalization （CLAHE） algorithm, a curvelet transform and contrast adaptive clip histogram equalization （HE）-based foggy day image enhancement algorithm is proposed. The proposed algorithm transforms an image to the curvelet domain and enhances the image detail information via a nonlinear transformation of high frequency curvelet coefficients. After curvelet reconstruction, the contrast adaptive clip HE method is adopted to enhance the total image contrast and the foggy day image contrast and detail information. During the histogram clipping process, the clip limit value is adaptively selected based on image contrast and the sub-block image histogram variance. A comparative analysis of the foggy day image enhancement results are obtained by applying CLAHE, and some classical single image defogging algorithms and the proposed algorithm are also conducted to prove the effectiveness of the proposed algorithm with objective parameters.

基于curvelet变换的红外图像去噪方法研究

针对传统小波算法所生成的可分离小波只具有有限的方向,不利于图像去噪,本文采用curvelet算法对红外图像进行去噪处理.该方法以小波变换为基础对图像进行分解,在分解所得的一系列小波子带中,以定义的平滑窗函数对曲线边缘进行平滑分割,再对平滑分割处理的每个子块进行Ridgelet变换.最后将小波阈值范围外的系数置零,以curvelet逆变换对原始图像进行重构.红外图像的去噪实验表明,本文算法有效可行,相比传统的小波算法、SVD算法,本算法能获得更高的PSNR数值,去噪效果更佳.

采用曲波变换和电容层析成像的两相流含水率测量方法

针对电容层析成像(ECT)逆问题的欠定性,提出了一种基于曲波变换的图像稀疏重构算法。首先,采用曲波变换基作为稀疏基,从多尺度对模拟介质分布图像进行稀疏表示,提高了ECT灰度值向量的稀疏度;其次,应用迭代软阈值算法对曲波系数进行稀疏重构,同时使用奇异值分解算法计算最优灵敏度广义逆矩阵;最后,搭建ECT测量平台,通过静态实验重构5种典型实际流型,验证所提算法的有效性。实验结果表明:所提算法可有效抑制伪影,重构图像边界清晰,同时在图像相关系数、相对图像误差上均优于传统重构算法,比Landweber迭代算法相关系数提高约37.9%,相对误差减小约17.6%;基于稀疏重构的后处理图像可用于截面含水率的测量,含水率平均误差低于10%,最低为4.32%。该方法有效提高了ECT重构图像质量,能够满足天然气精准开采的工业需求。

基于Curvelet变换的缺失地震数据插值方法

Curvelet变换具有局部性、多尺度性和多方向性,在处理非稳态地震信号中具有较大的优势,可以利用Curvelet变换的压缩特性重建缺失的地震数据。本文首先分析了基于稀疏变换的缺失地震数据插值的基本原理,在反问题正则化理论框架下,针对L2范数约束和L1范数约束条件,分析了两种约束的差异,着重阐述了基于Curvelet变换的L1范数约束的插值方法,其优点在于对非线性同相轴不需要分窗口处理,并将凸集投影算法(POCS)引入到Curvelet变换的插值方法中,通过采用按指数规律衰减的阈值参数加快了迭代收敛的速度。理论和实际算例验证了Curvelet变换插值方法的有效性。

基于Curvelet域的叠前地震资料去噪方法

本文论述了基于Curvelet域的叠前地震资料去除随机噪声的方法思路,在Curvelet域通过多尺度分解,在曲线变化特征良好逼近的基础上采用非线性阈值方法,对叠前地震资料进行处理。通过本文的数值计算以及对实际资料的处理表明,基于Curvelet域的去噪方法可以有效压制地震剖面中的随机噪声,数值计算结果均方误差最小,信噪比最高,实际地震处理剖面视觉效果好,反射波同相轴清晰、连续性好,可为叠前偏移、AVO分析等后续处理解释提供高信噪比的地震资料。