Skin Lesion Classification System Using Shearlets

The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

An Automated Brain Image Analysis System for Brain Cancer using Shearlets

In this paper,an Automated Brain Image Analysis(ABIA)system that classifies the Magnetic Resonance Imaging(MRI)of human brain is presented.The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis.The Non-Subsampled Shearlet Transform(NSST)that captures more visual information than conventional wavelet transforms is employed for feature extraction.As the feature space of NSST is very high,a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies.A combination of features that includes Gray Level Co-occurrence Matrix(GLCM)based features,Histograms of Positive Shearlet Coefficients(HPSC),and Histograms of Negative Shearlet Coefficients(HNSC)are estimated.The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers;k-Nearest Neighbor(kNN),Naive Bayes(NB)and Support Vector Machine(SVM)classifiers.The output of individual trained classifiers for a testing input is hybridized to take a final decision.The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data(REMBRANDT)database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.

基于鲁棒纹理特征的环境声音事件检测方法

针对各种类别的环境声音事件检测问题,提出了基于鲁棒纹理特征的环境声音事件检测方法。首先,将原始的声音样本转换为类伽马声谱图;然后将类伽马声谱图通过剪切波变换提取图像的纹理特征;又采用中心化二值模式(CBP)算法进行编码;针对特征维度过高问题,先利用随机森林算法后结合主成分分析(PCA)算法,提出了RF-PCA降维方法;最后使用支持向量机(SVM)对不同环境的声音进行分类。在公开数据集ESC-10上的仿真实验结果表明,利用所提出的基于鲁棒纹理特征的环境声音事件检测方法所提取的特征对声音分类可达到93.00%的分类效果。

联合VMD和Shearlet变换的去噪方法研究

地震数据中的噪声信号会很大程度上降低地震数据的信噪比,对后续的处理和解释工作造成一定的难度,因此研究一种去噪方法具有十分重要的意义。本文构建一种变分模态分解(Variational Mode Decomposition,VMD)和Shearlet变换联合去噪的方法,首先通过VMD将地震数据分解为一系列不同中心频率分布的模态分量(Intrinsic Mode Function,IMF),然后使用Shearlet变换对含噪的IMF分量进行去噪并对处理后的分量进行重构,最终达到去噪目的。该方法引入基于稀疏表示的地震数据去噪方法,兼顾VMD和Shearlet变换的优点,可以有效去除噪声。经过合成信号、模型及实际数据测试结果表明,本文方法处理合成信号结果显示,与VMD和小波变换方法相比,信噪比分别提高1.69、1.87,均方误差数值上减少近一半,在去除噪声的同时能更好地保留地震数据特征,提高地震资料的信噪比。

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

Applying the Shearlet-Based Complexity Measure for Analyzing Mass Transfer in Continuous-Flow Microchannels

Continuous-flow microchannels are widely employed for synthesizing various materials,including nanoparticles,polymers,and metal-organic frameworks(MOFs),to name a few.Microsystem technology allows precise control over reaction parameters,resulting in purer,more uniform,and structurally stable products due to more effective mass transfer manipulation.However,continuous-flow synthesis processes may be accompanied by the emergence of spatial convective structures initiating convective flows.On the one hand,convection can accelerate reactions by intensifying mass transfer.On the other hand,it may lead to non-uniformity in the final product or defects,especially in MOF microcrystal synthesis.The ability to distinguish regions of convective and diffusive mass transfer may be the key to performing higher-quality reactions and obtaining purer products.In this study,we investigate,for the first time,the possibility of using the information complexity measure as a criterion for assessing the intensity of mass transfer in microchannels,considering both spatial and temporal non-uniformities of liquid’s distributions resulting from convection formation.We calculate the complexity using shearlet transform based on a local approach.In contrast to existing methods for calculating complexity,the shearlet transform based approach provides a more detailed representation of local heterogeneities.Our analysis involves experimental images illustrating the mixing process of two non-reactive liquids in a Y-type continuous-flow microchannel under conditions of double-diffusive convection formation.The obtained complexity fields characterize the mixing process and structure formation,revealing variations in mass transfer intensity along the microchannel.We compare the results with cases of liquid mixing via a pure diffusive mechanism.Upon analysis,it was revealed that the complexity measure exhibits sensitivity to variations in the type of mass transfer,establishing its feasibility as an indirect criterion for assessing mass transfer intensity.The method presented can extend beyond flow analysis,finding application in the controlling of microstructures of various materials(porosity,for instance)or surface defects in metals,optical systems and other materials that hold significant relevance in materials science and engineering.

Estimations of directional Hlder regularity by shearlets

This paper studies directional Hlder regularity of two-variable functions by their shearlet coefficients, where the shearlets are defined by Guo and Labate(2013). We provide necessary conditions for a function possessing some directional H¨older regularity and the corresponding sufficient conditions, motivated by the work of Sampo and Sumetkijakan(2009) and Lakhonchai et al.(2010).

基于多尺度卷积神经网络和LBP算法的浮选工况识别

针对泡沫浮选加药状态检测困难、识别效率低和主观性强等问题,提出了一种结合多尺-度卷积神经网络(CNN)特征及改进局部二值模式(LBP)计算方法的核随机权神经网络(K RV-FLNs)浮选工况识别方法。首先,对泡沫浮选图像进行非下采样Shearlet多尺度分解,将原始图像分解为不同频率尺度,设计多通道CNN网络对多尺度图像进行特征提取;再通过改进LBP算法提取特征作为补充,将CNN提取的图像特征与LBP特征进行融合;最后,通过核随机权神经网络映射到更高维空间进行分类决策,实现浮选加药状态的精确识别。实验结果表明,采用多尺度CNN及LBP-TOP特征融合的方法识别的精度比传统LBP算法提高了5.34%,比采用单CNN特-征的方法提高了3.76%,结合K RVFLNs实现浮选工况分类准确率高达96.38%,识别精度和稳定性较现有方法有较大提升,且减少了人工干预,有利于提高生产效率。

基于Shearlet变换的非局部均值地震噪声压制

在地震勘探中,由于野外地震数据采集环境及仪器性能本身的限制,采集到地震信号中不可避免地会混入较强的噪声,极大影响后续处理、解释工作。而近几年,多尺度几何分析方法以其独特优势成为压制噪声的研究热点,本文提出在Shearlet域中引入非局部均值算法对地震噪声进行压制,该算法首先对地震信号进行非下采样Shearlet变换,然后采用非局部均值法对分解后系数子集进一步处理,并采用8个Sobel算子近似表示全方向结构,对权重函数进行改进,最后对系数进行Shearlet反变换,得到去噪后的地震信号。实验结果表明相比于传统非局部均值法,该联合算法能有效地压制随机噪声,同时对弱同相轴具有更好的保护作用,在地震资料处理中具有良好的实用性。

一种基于QR码混沌图像加密算法研究

随着资讯传播互联网化的程度越来越深,线上图像资源的版权保护受到越来越多的重视。该文提出了一种基于QR码(Quick response code)的利用Shearlet变换和线性螺旋变换的混沌图像数字水印算法。首先,对预加密图像资源生成版权QR码。其次,用Arnold变换对QR码进行加扰。最后,用Shearlet变换、线性螺旋变换和离散余弦变换将版权水印信息嵌入图像的中频部分,以嵌入水印。提取则是嵌入过程的逆过程。经试验证明,该文提出的方法不仅具有较好的版权信息隐蔽性,而且还有较强的鲁棒性。嵌入的QR码对一定比例的图像遮挡和噪声具有抗攻击能力。

基于非下采样Shearlet变换耦合能量关联度的医学图像融合算法

为了克服当前较多医学图像融合方法在采用图像的能量信息融合图像时,忽略了不同图像能量的关联度,使得融合结果存在细节丢失现象和模糊现象等问题,提出了一种非下采样Shearlet变换(NSST,non-subsampled shearlet transform)耦合能量关联度的医学图像融合算法;借助NSST变换,在多尺度下对输入医学图像进行解析,获取其低频及高频子带系数;以图像的能量信息为依据,构造能量关联度函数,测量不同图像的关联程度;根据不同图像的关联度,设计不同的低频子带融合规则,获取信息含量丰富且连贯性较好的融合低频子带;在空间频率函数的基础上,注入图像的对角信息,使之成为多元空间频率函数,以计算图像的清晰度;引入标准差函数,计算图像的对比度;联合图像的清晰度和对比度信息,获取纹理及对比度等特征都较优良的融合高频子带;基于逆NSST变换,重构融合结果;主观和客观实验结果表明,较当前较为流行的医学图像融合技术而言,所提方法具备更高的融合质量,呈现出更多的纹理细节和更高的清晰度。

Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

With the development of digitalization in healthcare,more and more information is delivered and stored in digital form,facilitating people’s lives significantly.In the meanwhile,privacy leakage and security issues come along with it.Zero watermarking can solve this problem well.To protect the security of medical information and improve the algorithm’s robustness,this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform(NSST)and Schur decomposition.Firstly,the low-frequency subband image of the original medical image is obtained by NSST and chunked.Secondly,the Schur decomposition of low-frequency blocks to get stable values,extracting the maximum absolute value of the diagonal elements of the upper triangle matrix after the Schur decom-position of each low-frequency block and constructing the transition matrix from it.Then,the mean of the matrix is compared to each element’s value,creating a feature matrix by combining perceptual hashing,and selecting 32 bits as the feature sequence.Finally,the feature vector is exclusive OR(XOR)operated with the encrypted watermark information to get the zero watermark and complete registration with a third-party copyright certification center.Experimental data show that the Normalized Correlation(NC)values of watermarks extracted in random carrier medical images are above 0.5,with higher robustness than traditional algorithms,especially against geometric attacks and achieve watermark information invisibility without altering the carrier medical image.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

图像边缘信息检测的一种有效方法

本文使用了一种新的后小波分析工具—Shearlets,利用它在高维情况下的多尺度性、方向敏感性等良好性质,对图像边缘进行精确的定位,以及确定边缘的类型。在此图像边缘检测过程中,用K-means聚类算法确定了边缘点和边缘的类型。最后进行实验,结果表明用Shearlets做图像边缘检测可以获得较好的效果。

基于Shearlet变换的地震随机噪声压制

地震勘探中的噪声对地震信号产生严重的畸变和干扰,常规的地震去噪方法已经不能满足当前高精度地震勘探的要求。提出了基于Shearlet变换的地震数据去噪方法,Shearlet变换是一种新的多尺度变换方法,具有多方向、多分辨率及最佳稀疏逼近性质,并且计算效率高。Shearlet变换在去除随机噪声的同时能最大程度保留有效信号,有效地提高信噪比。利用Shearlet变换阈值去噪法与其他地震去噪方法分别对不同信噪比的合成地震记录和实际地震记录进行对比,结果表明Shearlet变换具有更强的去噪能力和更高的运算效率。

基于Shearlet变换的图像去噪算法

针对传统变换域去噪算法的不足,提出一种基于Shearlet变换的图像去噪算法。该算法首先在Shearlet变换理论基础上实现了一种分解和重构的方法,然后用Monte-Carlo方法对高频系数进行估计,最后通过阈值函数进行收缩去噪。实验结果表明,该算法在抑噪和保持边缘的同时,取得了较好的视觉效果和更高的PSNR值。

基于压缩感知技术的Shearlet变换重建地震数据

基于预测滤波方法进行地震数据重建的误差偏大,基于波动方程进行地震数据重建的计算量较大,基于某种变换的地震数据重建精度偏低。为此,利用基于压缩感知技术的Shearlet变换重建地震数据。基于信号的稀疏性,在欠采样的情况下,首先根据地震数据的缺失情况设计采样矩阵,然后使用Shearlet变换将地震数据稀疏化,再采用正交匹配追踪算法在Shearlet域中完成对稀疏系数的重建,最后通过Shearlet反变换实现地震数据重建。实验结果表明,基于压缩感知技术的Shearlet变换能够很好地重建地震数据,且重建精度高于基于压缩感知技术的Fourier变换、离散余弦变换、小波变换和Curvelet变换。

基于Shearlet变换的自适应图像融合算法

针对多聚焦图像与多光谱和全色图像的成像特点,结合Shearlet变换具有较好的稀疏表示图像特征的性质,提出了一种新的图像融合规则.并基于此融合规则,提出了基于Shearlet变换的自适应图像融合算法.在多聚焦图像的融合算法中,分别对聚焦不同的图像进行Shearlet变换,并基于本文提出的融合规则,对分解后的高低频系数进行融合处理.通过与多种算法的比较实验证明了本文提出的算法融合的图像具有更高的清晰度和更加丰富的细节信息.在多光谱和全色图像的融合处理中,提出了一种基于Shearlet变换与HSV变换相结合的图像融合方法.该算法首先对多光谱图像作HSV变换,将得到的V分量与全色图像进行Shearlet分解与融合,在融合过程中对分解系数选用特定的融合准则进行融合,最后将融合生成新的分量与H、S分量进行HSV逆变换产生新的RGB融合图像.该算法在空间分辨率和光谱特性两方面达到了良好的平衡,融合后的图像在减少光谱失真的同时,有效增强了空间分辨率.仿真实验证明,本文算法融合的图像与传统的多光谱和全色图像融合算法相比,具有更佳的融合性能和视觉效果.

基于Shearlet变换稀疏约束地震数据重建

地震数据重建是地震数据处理流程中关键步骤之一,重建效果的好坏直接影响到后续的多次波消除以及偏移成像效果。为了获得更好的重建效果,提出了以压缩感知为理论基础,采用jitter欠采样的Shearlet变换稀疏约束地震数据重建方法。将Shearlet变换与凸集投影(POCS)算法结合起来在动校正预处理后对地震数据进行重建,增强了地震数据在Shearlet域的稀疏性。理论分析和实际地震数据验证结果表明,该方法可以在部分地震数据缺失的情况下取得很好的重建效果,有效地解决了假频问题。

基于粒子群优化的Shearlet自适应图像去噪

研究Shearlet变换域图像去噪阈值选取的问题,提出Shearlet变换域图像去噪自适应阈值选取方法.该方法根据Shear-let变换域不同尺度和方向系数的分布特性,采用粒子群优化算法自适应地确定各尺度和方向的最优阈值,实现基于图像内容的自适应去噪.仿真实验表明,该方法能有效滤除图像的噪声,较好地保留图像的边缘信息.同时,去噪后图像具有更高的峰值信噪比(PSNR).