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 co...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.展开更多
In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusi...In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.展开更多
Shearlet变换作为后小波时代的一个重要的多尺度几何分析工具具有良好的各向异性和方向捕捉性,同时它也可以对诸如图像等多维信号进行一种近最优的稀疏表示.非下采样Shearlet变换(NSST)在保持Shearlet变换特性的同时还具有平移不变特性...Shearlet变换作为后小波时代的一个重要的多尺度几何分析工具具有良好的各向异性和方向捕捉性,同时它也可以对诸如图像等多维信号进行一种近最优的稀疏表示.非下采样Shearlet变换(NSST)在保持Shearlet变换特性的同时还具有平移不变特性,这在具有丰富纹理和细节信息的图像处理中发挥着重要作用.该文首先对图像NSST方向子带内系数的概率密度分布进行分析,获得系数的稀疏统计特性和Cauchy分布拟合子带内系数的有效性;其次对NSST方向子带间系数的联合概率分布进行分析,获得方向子带系数间所具有的持续和传递特性,确定了一种NSST子带间树形架构的系数对应关系,进而提出一种NSST域隐马尔可夫模树模型(C-NSSTHMT),该模型通过Cauchy分布来拟合NSST系数,更好地揭示图像NSST变换后相同尺度子带内和不同尺度子带间系数的相关性.进一步提出一种基于所提出C-NSST-HMT模型的图像去噪算法,该算法对于含噪声方差为30和40的噪声图像,其去噪后的PSNR(Peak Signal to Noise Ratio)较NSCT-HMT方法分别提高了1.995dB和1.193dB.特别对纹理和细节丰富的图像,该算法在去噪的同时,有效地保留了图像的几何信息.展开更多
基金supported in part by the Natural Science Foundation of China under Grants 62063004the Key Research Project of Hainan Province under Grant ZDYF2021SHFZ093+1 种基金the Hainan Provincial Natural Science Foundation of China under Grants 2019RC018 and 619QN246the postdoctoral research from Zhejiang Province under Grant ZJ2021028.
文摘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.
基金Open Fund Project of Key Laboratory of Instrumentation Science&Dynamic Measurement(No.2DSYSJ2015005)Specialized Research Fund for the Doctoral Program of Ministry of Education Colleges(No.20121420110004)
文摘In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.
文摘Shearlet变换作为后小波时代的一个重要的多尺度几何分析工具具有良好的各向异性和方向捕捉性,同时它也可以对诸如图像等多维信号进行一种近最优的稀疏表示.非下采样Shearlet变换(NSST)在保持Shearlet变换特性的同时还具有平移不变特性,这在具有丰富纹理和细节信息的图像处理中发挥着重要作用.该文首先对图像NSST方向子带内系数的概率密度分布进行分析,获得系数的稀疏统计特性和Cauchy分布拟合子带内系数的有效性;其次对NSST方向子带间系数的联合概率分布进行分析,获得方向子带系数间所具有的持续和传递特性,确定了一种NSST子带间树形架构的系数对应关系,进而提出一种NSST域隐马尔可夫模树模型(C-NSSTHMT),该模型通过Cauchy分布来拟合NSST系数,更好地揭示图像NSST变换后相同尺度子带内和不同尺度子带间系数的相关性.进一步提出一种基于所提出C-NSST-HMT模型的图像去噪算法,该算法对于含噪声方差为30和40的噪声图像,其去噪后的PSNR(Peak Signal to Noise Ratio)较NSCT-HMT方法分别提高了1.995dB和1.193dB.特别对纹理和细节丰富的图像,该算法在去噪的同时,有效地保留了图像的几何信息.
