Quantum Hilbert Image Scrambling

Analogies between quantum image processing (QIP) and classical one indicate that quantum image scrambling (QIS), as important as quantum Fourier transform (QFT), quantum wavelet transform (QWT) and etc., should be proposed to promote QIP. Image scrambling technology is commonly used to transform a meaningful image into a disordered image by permutating the pixels into new positions. Although image scrambling on classical computers has been widely studied, we know much less about QIS. In this paper, the Hilbert image scrambling algorithm, which is commonly used in classical image processing, is carried out in quantum computer by giving the scrambling quantum circuits. First, a modified recursive generation algorithm of Hilbert scanning matrix is given. Then based on the flexible representation of quantum images, the Hilbert scrambling quantum circuits, which are recursive and progressively layered, is proposed. Theoretical analysis indicates that the network complexity scales squarely with the size of the circuit’s input n.     

An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing

A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images.Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s).In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm.Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.     

Multi-sensor image fusion using discrete wavelet frame transform

An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform (DWFT).The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transform based or discrete wavelet transform (DWT) based algorithms in multi-sensor image fusion.     

基于分块DCT变换编码的小波域多幅图像融合算法

提出了一种利用DCT变换和小波变换的特征层图像融合算法.其基本思想是先对多幅源图像进行分块DCT变换,选取较大方差对应的变换系数,将图像压缩为原图像大小的1/4,保留系数的对应坐标作为提取信息时的密钥;其次将经处理后的DCT系数直接作为小波变换的分解系数,经小波逆变换后得到融合信息.实验结果表明,该算法实现了多幅不同大小图像的融合,同时单一密钥只能提取单一图像. 关键词： 图像融合 小波变换 离散余弦变换 编码     

基于区域分割和Counterlet变换的图像融合算法

提出了一种基于区域分割和Contourlet变换的图像融合算法。首先,对各源图像做区域分割,并利用区域能量比和区域清晰比的概念来度量和提取区域信息;然后,对各源图像进行多尺度非子采样Contourlet分解,分解后的高频部分采用绝对值取大算子进行融合,低频部分则采用基于区域的融合规则和算子进行融合;最后进行重构得到融合图像。对红外与可见光图像进行了融合实验,并与基于像素的àtrous小波变换和Contourlet变换的融合效果进行了比较。结果表明,采用本文算法的融合图像既保留了可见光图像的光谱信息,又继承了红外图像的目标信息,其熵值高于基于像素的融合方法约10%,交叉熵仅为基于像素的融合方法的1%左右。     

Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest

To effectively combine regions of interest in original infrared and visual images, an adaptively weighted infrared and visual image fusion algorithm is developed based on the multiscale top-hat selection transform. First, the multiscale top-hat selection transform using multiscale structuring elements with increasing sizes is discussed. Second, the image regions of the original infrared and visual images at each scale are extracted by using the multiscale top-hat selection transform. Third, the final fusion regions are constructed from the extracted multiscale image regions. Finally, the final fusion regions are combined into a base image calculated from the original images to form the final fusion result. The combination of the final fusion regions uses the adaptive weight strategy, and the weights are adaptively obtained based on the importance of the extracted features. In the paper, we compare seven image fusion methods: wavelet pyramid algorithm (WP), shift invariant discrete wavelet transform algorithm (SIDWT), Laplacian pyramid algorithm (LP), morphological pyramid algorithm (MP), multiscale morphology based algorithm (MSM), center-surround top-hat transform based algorithm (CSTHT), and the proposed multiscale top-hat selection transform based algorithm. These seven methods are compared over five different publicly available image sets using three metrics of spatial frequency, mean gradient, and Q. The results show that the proposed algorithm is effective and may be useful for the applications related to the infrared and visual image fusion.     

Performance analysis of multi-spectral and panchromatic image fusion techniques based on two wavelet discrete approaches

During the past few years, many fusion algorithms have been proposed to combine a high-resolution panchromatic image with a low-resolution multi-spectral image to generate a high-resolution multi-spectral image. Among them, the wavelet-based algorithm has gained its popularity due to its ability of multi-resolution decomposition. More specifically, the wavelet transform is first applied to images. The wavelet coefficients are then combined based on a certain rule to produce the fused image. In this paper, we evaluated the performances of both the wavelet transform discrete approaches and the coefficient combination methods when they are applied to fuse multi-spectral and panchromatic images. For the discrete approaches of the wavelet transform, Mallat and “à trous” algorithms are chosen. For the coefficient combination, the additive wavelet method, the additive wavelet intensity method and the additive wavelet principal component method are selected. To evaluate the spectral quality of the fused images, correlation coefficient and Q_avg index are used as a local and global measure, respectively. Meanwhile, average gradient and standard deviation are used to evaluate the spatial quality. Our experiments show that keeping the combination method the same, the “à trous” algorithm works better than the Mallat algorithm for the fusion purpose. In addition, if keeping the wavelet discrete algorithm the same, the combination methods mentioned above are found to have different advantages between the spatial resolution improvement and the spectral quality preservation.     

基于二代curvelet变换的图像融合研究

曲波(Curvelet)作为一种新的多尺度分析方法比小波更加适合分析二维图像中的曲线或直线状边缘特征,而且具有更高的逼近精度和更好的稀疏表达能力.将curvelet变换引入图像融合,能够更好地提取原始图像的特征,为融合图像提供更多的信息.第二代curvelet理论的提出也使得其理论更易理解和实现.因此,提出了一种基于第二代curvelet变换的图像融合方法,首先将图像进行curvelet变换,然后在相应尺度上利用融合规则将curvelet系数融合,最后进行重构得到融合结果.对多聚焦图像进行了实验,采用均方误差、偏差指数和相关系数对融合结果进行了客观评价,并与基于小波变换的融合进行了比较,实验结果表明该方法除分解2层时与小波性能相当,取其他分解层数时均获得更好的融合效果.     

基于平移不变剪切波变换域图像融合算法

针对传统基于多尺度变换的图像融合方法存在的缺点,提出了一种基于平移不变剪切波变换域的自适应图像融合新方法.首先,使用平移不变剪切波变换对源图像进行分解,得到低频子带及方向带通子带系数.然后,对于低频子带系数采用梯度域奇异值分解方法估计图像的局部结构信息,提出了基于提取的特征与S函数的可变加权融合策略;对于各方向带通子带系数,提出了一种基于改进的拉普拉斯能量和匹配的"加权平均"和选择相结合的系数选择策略.最后,对得到的融合系数进行逆变换得到融合图像.通过实验可以发现相比于传统的图像融合方法,本文方法得到了更高的客观指标,融合图像视觉效果更好.     

一种基于提升小波的图像融合技术研究

针对红外与可见光图像进行提升小波变换后低频图像的特点,提出了一种低频分量的融合算法,高频分量采取邻域方差取大为准则进行融合,然后进行提升小波逆变换得到融合图像。通过与传统小波融合方法进行比较,并引入信息熵、清晰度、Xydeas-Petrovic客观性能指标对融合后的图像进行分析。实验结果表明不论从视觉效果还是从客观性能指标上,该算法都优于传统的融合方法。     

基于可见光的多波段偏振图像融合新算法

采用了一种新的基于小波变换的偏振图像融合算法.首先,将两个波段中的每一波段三幅偏振图像利用小波变换分解成低频和高频部分,低频的小波系数平均值作为融合后的低频系数,高频细节系数根据不同区域特征选择方法以及对应输入图像小波系数的窗口区域方差来确定融合后高频小波系数,得到一个波段一幅图像.接着,将得到的图像再进行小波分解,采用低频图像的小波系数最小值作为融合后的低频系数,高频图像根据纹理一致性测度的纹理检测确定融合规则,用来调整高频小波系数,将来自不同图像的特征与细节融合在一起,并对融合图像质量进行了对比评价.实验结果表明,融合后的偏振图像不仅反映了场景的偏振信息,而且还包含了丰富的光谱信息,目标与背景的衬比度也得到了增强,为进一步的目标检测和识别提供了便利.     

Feature-based fusion of infrared and visible dynamic images using target detection

We employ the target detection to improve the performance of the feature-based fusion of infrared and visible dynamic images, which forms a novel fusion scheme. First, the target detection is used to segment the source image sequences into target and background regions. Then, the dual-tree complex wavelet transform (DT-CWT) is proposed to decompose all the source image sequences. Different fusion rules are applied respectively in target and background regions to preserve the target information as much as possible. Real world infrared and visible image sequences are used to validate the performance of the proposed novel scheme. Compared with the previous fusion approaches of image sequences, the improvements of shift invariance, temporal stability and consistency, and computation cost are all ensured.     

一种基于非下采样Contourlet变换多聚焦图像融合算法

针对现有小波类图像融合算法的不足,提出了一种基于非下采样Contourlet变换多聚焦图像融合算法,并在Contourlet域中引入了局部区域可见度以及局部方向能量的概念.针对低频子带系数和各带通方向子带系数分别提出了基于局部区域可见度以及基于局部方向能量的系数选择方案.通过对多聚焦图像融合的仿真实验,表明该算法相对于传统的基于离散小波变换和离散小波框架变换融合算法能够有效减少有用信息的丢失以及虚假信息的引入,同时能够从源图像中提取更多的有用信息并注入到融合图像中, 得到更好视觉效果和更优量化指标的融合图像.     

基于PHLST的红外与可见光图像融合算法

针对图像融合过程中边缘处理和区域一致性的问题,提出一种基于多重调和局部正弦变换的红外与可见光图像融合新算法.多重调和局部正弦变换的多重调和分量μ代表了图像缓慢变化的"趋势",在空域进行加权融合;残差分量υ体现了源图像的"波动",在傅里叶正弦变换域进行融合,以充分提取可见光图像的细节信息.由于不存在边缘效应,同时残差分量...     

A novel video fusion framework using surfacelet transform

A novel video fusion framework based on the three-dimensional surfacelet transform (3D-ST) is proposed in this paper. Different from the traditional individual-frame based video fusion methods, the proposed framework fused multi-frame images of input videos as a whole rather than frame by frame independently with the 3D-ST. Furthermore, under the proposed framework, two ST-based video fusion algorithms are proposed. In the first algorithm, no special treatment is performed on the temporal motion information in input videos, and only a spatial-temporal region energy-based fusion rule is employed. While in the second algorithm, a modified z-score based motion detection is performed to distinguish the temporal motion information from the spatial geometry information, and then a motion-based fusion rule is present. Experimental results demonstrate that, with the motion selectivity of the 3D-ST, existing static image fusion rules can be extended to video fusion under the proposed framework. Both of the two proposed fusion algorithms significantly outperform some traditional individual-frame based and motion-based methods in spatial-temporal information extraction as well as in temporal stability and consistency. In addition, the second proposed algorithm is with high computation efficiency and can be applied to real-time video fusion.     

A novel image fusion algorithm based on nonsubsampled shearlet transform

To overcome the shortcoming of traditional image fusion method based on multi-scale transform, a novel adaptive image fusion algorithm based on nonsubsampled shearlet transform (NSST) is proposed. Firstly, the NSST is utilized to decompose the source images on various scales and in different directions, and the low frequency sub-band and bandpass sub-band coefficients are obtained. Secondly, for the low frequency sub-band coefficients, the singular value decomposition method in the gradient domain is used to estimate the local structure information of image, and an adaptive ‘weighted averaging’ fusion rule based on the sigmoid function and the extracted features is presented. To improve the quality of fused image, a novel sum-modified-Laplacian (NSML), which can extract more useful information from source images, is employed as the measurement to select bandpass sub-band coefficients. Finally, the fused image is obtained by performing the inverse NSST on the combined coefficients. The proposed fusion method is verified on several sets of multi-source images, and the experimental results show that the proposed approach can significantly outperform the conventional image fusion methods in terms of both objective evaluation criteria and visual quality.     

基于非采样提升小波-Contourlet变换的图像融合算法

为克服非采样Contourlet变换中金字塔分解的不足,首先在提升小波变换的基础上,通过取消其奇偶分裂环节得到具有平移不变性的非采样提升小波变换,然后用此变换来取代非采样Contourlet变换中的金字塔分解,得到新的非采样提升小波-Contourlet变换。将此变换与一定的融合规则相结合,提出了一种基于非采样提升小波-Contourlet变换的图像融合算法。实验表明,该算法相对于非采样Contourlet变换能从源图像中提取更多有用信息注入到融合图像中,可得到更高性能的融合图像。     

Quantum Multi-Image Encryption Based on Iteration Arnold Transform with Parameters and Image Correlation Decomposition

A novel quantum multi-image encryption algorithm based on iteration Arnold transform with parameters and image correlation decomposition is proposed, and a quantum realization of the iteration Arnold transform with parameters is designed. The corresponding low frequency images are obtained by performing 2-D discrete wavelet transform on each image respectively, and then the corresponding low frequency images are spliced randomly to one image. The new image is scrambled by the iteration Arnold transform with parameters, and the gray-level information of the scrambled image is encoded by quantum image correlation decomposition. For the encryption algorithm, the keys are iterative times, added parameters, classical binary and orthonormal basis states. The key space, the security and the computational complexity are analyzed, and all of the analyses show that the proposed encryption algorithm could encrypt multiple images simultaneously with lower computational complexity compared with its classical counterparts.     

结合小波域变换和空间域变换的图像增强方法

提出了一种结合了小波域和空间域处理方法的图像增强算法. 该算法首先对小波域中的高频系数进行修正,使图像具有更好的局部对比度和更丰富的细节,由于双树复小波变换（Dual-tree Complex Wavelet Transform,DT CWT）具有更好的方向选择性,在小波变换的过程中选用了这一方法;然后,通过空间域中的非线性变换,调整图像的整体对比度. 该算法可根据图像本身的特性实现参数的自动选择. 经过本文方法的处理所得的图像,无论在视觉效果上还是在统计上,都优于前人工作的结果.     

一种基于小波变换的红外偏振融合算法

针对红外偏振图像可以较好地抑制背景噪声,对目标边缘信息比较敏感的特点,提出一种基于小波变换的红外偏振融合算法,它主要用于红外辐射强度图像和偏振度图像融合,增加图像的信息量。首先采用小波变换对参与融合的每幅图像分别进行各尺度分解,得到各尺度小波系数,然后针对不同尺度小波系数,采用邻域平均梯度为判据进行融合,得到融合后的各尺度小波系数,最后通过小波逆变换进行图像重构,得到融合图像。融合前后的图像对比表明融合图像在保留辐射强度图像的清晰度的同时,突出了目标的边缘、轮廓信息。相对于辐射强度图像,融合图像的梯度均值提高了112%,相对于偏振度图像,融合图像的标准差提高了151%,信息熵提高了38%。