有限离散剪切波域的红外可见光图像融合

针对目前图像融合过程中的不足之处,结合有限离散剪切波具有高的方向敏感性和抛物尺度化特性,提出了一种有限离散剪切波变换下的图像融合算法。首先对严格配准的多传感器图像进行有限离散剪切波变换,得到低频子带系数和不同尺度不同方向的高频子带系数;然后对低频子带系数采用全局特征值和像素点之间的差异性与区域空间频率匹配度相结合的融合算法,高频方向子带系数采用方向权重对比度与相对区域平均梯度和相对区域方差相结合的方案;最后通过有限离散剪切波逆变换得到融合图像。实验结果表明,与其他的融合算法相比较,本文算法不但有良好的主观视觉效果,而且3幅图像的客观评价指标分别平均提高了0.9%、3.8%、3.1%,2.6%、3.8%、2.9%和1.5%、125%、59%,充分说明了本文融合算法的优越性。     

Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.     

Inferring Users’ Social Roles with a Multi-Level Graph Neural Network Model

Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.     

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.     

Encryption of color images using MSVD in DCST domain

In this paper, a new image encryption and decryption algorithm based on Multiresolution Singular Value Decomposition (MSVD) and Discrete Cosine Stockwell Transform (DCST) is proposed. An original image is first transformed in DCST domain and then decomposed into four subbands using MSVD, all the four subbands are further decomposed into four subimages according to their indexing and masked by the parameters generated by MSVD. We have used number of bands of DCST, arrangement of MSVD subbands, arrangement of various subimages obtained from MSVD subbands, values and arrangement of a 4×4 matrix generated by MSVD and the arrangement of masked subimages as encryption and decryption keys. To ensure the correct decryption of encrypted image, it is indeed necessary to have correct knowledge of all keys in correct order along with their exact values. If all the keys are correct but a single key is wrong even though it would be almost impossible to guess the original image. The efficiency of proposed algorithm is evaluated by comparing it with some recent published works and it is evident from the experimental results and analysis that the proposed algorithm can transmit the images more securely and efficiently over the network.     

Improved grid-noise removal in single-frame digital moiré 3D shape measurement

A single-frame grid-noise removal technique was developed for application in single-frame digital-moiré 3D shape measurement. The ability of the stationary wavelet transform (SWT) to prevent oscillation artifacts near discontinuities, and the ability of the Fourier transform (FFT) applied to wavelet coefficients to separate grid-noise from useful image information, were combined in a new technique, SWT-FFT, to remove grid-noise from moiré-pattern images generated by digital moiré. In comparison to previous grid-noise removal techniques in moiré, SWT-FFT avoids the requirement for mechanical translation of optical components and capture of multiple frames, to enable single-frame moiré-based measurement. Experiments using FFT, Discrete Wavelet Transform (DWT), DWT-FFT, and SWT-FFT were performed on moiré-pattern images containing grid noise, generated by digital moiré, for several test objects. SWT-FFT had the best performance in removing high-frequency grid-noise, both straight and curved lines, minimizing artifacts, and preserving the moiré pattern without blurring and degradation. SWT-FFT also had the lowest noise amplitude in the reconstructed height and lowest roughness index for all test objects, indicating best grid-noise removal in comparison to the other techniques.     

涂层目标1.06μm偏振双向反射分布函数测量方法研究

为了获取复杂涂层目标的光学散射特性,介绍了一套全自动偏振双向反射分布函数测量系统,可以测量半球空间内几乎所有角度下的偏振双向反射分布函数。采用双波片旋转法实现了对光的偏振调制,利用离散傅里叶变换反演出了涂层目标的Muller矩阵。给出了某涂层样品的测量结果与模型的计算比较。结果表明,这种方法是一种测量涂层目标偏振双向反射分布函数的有效方法。     

A New Algorithm for Digital Image Encryption Based on Chaos Theory

In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme’s effectiveness and show that this technique is a suitable choice for actual image encryption.     

Distinguishability and Disturbance in the Quantum Key Distribution Protocol Using the Mean Multi-Kings’ Problem

We introduce a quantum key distribution protocol using mean multi-kings’ problem. Using this protocol, a sender can share a bit sequence as a secret key with receivers. We consider a relation between information gain by an eavesdropper and disturbance contained in legitimate users’ information. In BB84 protocol, such relation is known as the so-called information disturbance theorem. We focus on a setting that the sender and two receivers try to share bit sequences and the eavesdropper tries to extract information by interacting legitimate users’ systems and an ancilla system. We derive trade-off inequalities between distinguishability of quantum states corresponding to the bit sequence for the eavesdropper and error probability of the bit sequence shared with the legitimate users. Our inequalities show that eavesdropper’s extracting information regarding the secret keys inevitably induces disturbing the states and increasing the error probability.     

Wind-Direction Estimation from Single X-Band Marine Radar Image Improvement by Utilizing the DWT and Azimuth-Scale Expansion Method

In this study, a method based on the discrete wavelet transform (DWT) and azimuth-scale expansion is presented to retrieve the sea-surface wind direction from a single X-band marine radar image. The algorithm first distinguishes rain-free and rain-contaminated radar images based on the occlusion zero-pixel percentage and then discards the rain-contaminated images. The radar image whose occlusion areas have been removed is decomposed into different low-frequency sub-images by the 2D DWT, and the appropriate low-frequency sub-image is selected. Images collected with a standard marine HH-polarized X-band radar operating at grazing incidence display a single intensity peak in the upwind direction. To overcome the influence of the occlusion area, before determining the wind direction, the data near the ship bow are shifted to expand the azimuth scale of the data. Finally, a harmonic function is least-square-fitted to the range-averaged radar return of the low-frequency sub-image as a function of the antenna look azimuth to determine the wind direction. Different from the wind-direction retrieval algorithms previously presented, this method is more suitable for sailing ships, as it functions well even if the radar data are heavily blocked. The results show that compared with the single-curve fitting algorithm, the algorithm based on DWT and azimuth-scale expansion can improve the wind-direction results in sailing ships, showing a reduction of 7.84° in the root-mean-square error with respect to the reference.     

Lossless Medical Image Compression by Using Difference Transform

This paper introduces a new method of compressing digital images by using the Difference Transform applied in medical imaging. The Difference Transform algorithm performs the decorrelation process of image data, and in this way improves the encoding process, achieving a file with a smaller size than the original. The proposed method proves to be competitive and in many cases better than the standards used for medical images such as TIFF or PNG. In addition, the Difference Transform can replace other transforms like Cosine or Wavelet.     

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.     

Image Statistics Preserving Encrypt-then-Compress Scheme Dedicated for JPEG Compression Standard

In this paper, the authors analyze in more details an image encryption scheme, proposed by the authors in their earlier work, which preserves input image statistics and can be used in connection with the JPEG compression standard. The image encryption process takes advantage of fast linear transforms parametrized with private keys and is carried out prior to the compression stage in a way that does not alter those statistical characteristics of the input image that are crucial from the point of view of the subsequent compression. This feature makes the encryption process transparent to the compression stage and enables the JPEG algorithm to maintain its full compression capabilities even though it operates on the encrypted image data. The main advantage of the considered approach is the fact that the JPEG algorithm can be used without any modifications as a part of the encrypt-then-compress image processing framework. The paper includes a detailed mathematical model of the examined scheme allowing for theoretical analysis of the impact of the image encryption step on the effectiveness of the compression process. The combinatorial and statistical analysis of the encryption process is also included and it allows to evaluate its cryptographic strength. In addition, the paper considers several practical use-case scenarios with different characteristics of the compression and encryption stages. The final part of the paper contains the additional results of the experimental studies regarding general effectiveness of the presented scheme. The results show that for a wide range of compression ratios the considered scheme performs comparably to the JPEG algorithm alone, that is, without the encryption stage, in terms of the quality measures of reconstructed images. Moreover, the results of statistical analysis as well as those obtained with generally approved quality measures of image cryptographic systems, prove high strength and efficiency of the scheme’s encryption stage.     

Implementation of an encrypted-compressed image wireless transmission scheme based on chaotic fractional-order systems

In this paper, an encryption, compression and transmission scheme is proposed. The scheme is based on fractional-order chaotic systems combined with Discrete Wavelet Transform (DWT) and Quadrature Phase Shift Keying (QPSK) modulation. The cipher performs rounds of digital operations between the vector states of the fractional-order system and the image to be encrypted. The implementation of the transmission process is made between a pair of software defined radio modules through a QPSK modulation scheme. The experimental results and security analysis show adequate performances for several applications in the presence of multiple attacks and environmental noise.     

Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user harvests energy from the primary users’ transmissions and opportunistically accesses the primary users’ licensed spectrum to deliver the status-update data pack. We aim to minimize the AoI subject to the energy causality and spectrum constraints by optimizing the sensing and update decisions. We formulate the AoI minimization problem as a partially observable Markov decision process and solve it via dynamic programming. Simulation results verify that our proposed policy is significantly superior to the myopic policy under different parameter settings.     

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.     

Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain

This paper presents a new multi-source image fusion scheme based on lifting stationary wavelet transform (LSWT) and a novel dual-channel pulse-coupled neural network (PCNN). By using LSWT, we can calculate a flexible multiscale and shift-invariant representation of registered images. After decomposing the original images using LSWT, a new dual-channel pulse coupled neural network, which can overcome some shortcomings of original PCNN for image fusion and putout the fusion image directly, is proposed and used for the fusion of sub-band coefficients of LSWT. In this fusion scheme, a new sum-modified-laplacian(NSML) of the low frequency sub-band image, which represent the edge-feature of the low frequency sub-band image in SLWT domain, is presented and input to motivate the dual-channel PCNN. For the fusion of high frequency sub-band coefficients, a novel local neighborhood modified-laplacian (LNML) measurement is developed and used as external stimulus to motivate the dual-channel PCNN. This fusion scheme is verified on several sets of multi-source images, and the experiments show that the algorithms proposed in the paper can significantly improve image fusion performance, compared with the fusion algorithms such as traditional wavelet, LSWT, and LSWT-PCNN in terms of objective criteria and visual appearance.     

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

The primary purpose of the reported research was to improve the discrete wavelet transform (DWT)-based JP3D compression of volumetric medical images by applying new methods that were only previously used in the compression of two-dimensional (2D) images. Namely, we applied reversible denoising and lifting steps with step skipping to three-dimensional (3D)-DWT and constructed a hybrid transform that combined 3D-DWT with prediction. We evaluated these methods using a test-set containing images of modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). They proved effective for 3D data resulting in over two times greater compression ratio improvements than competitive methods. While employing fast entropy estimation of JP3D compression ratio to reduce the cost of image-adaptive parameter selection for the new methods, we found that some MRI images had sparse histograms of intensity levels. We applied the classical histogram packing (HP) and found that, on average, it resulted in greater ratio improvements than the new sophisticated methods and that it could be combined with these new methods to further improve ratios. Finally, we proposed a few practical compression schemes that exploited HP, entropy estimation, and the new methods; on average, they improved the compression ratio by up to about 6.5% at an acceptable cost.     

Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform

A novel image fusion technique based on NSST (non-subsampled shearlet transform) is presented, aiming at resolving the fusion problem of spatially gray-scale visual light and infrared images. NSST, as a new member of MGA (multi-scale geometric analysis) tools, possesses not only flexible direction features and optimal shift-invariance, but much better fusion performance and lower computational costs compared with several current popular MGA tools such as NSCT (non-subsampled contourlet transform). We specifically propose new rules for the fusion of low and high frequency sub-band coefficients of source images in the second step of the NSST-based image fusion algorithm. First, the source images are decomposed into different scales and directions using NSST. Then, the model of region average energy (RAE) is proposed and adopted to fuse the low frequency sub-band coefficients of the gray-scale visual light and infrared images. Third, the model of local directional contrast (LDC) is given and utilized to fuse the corresponding high frequency sub-band coefficients. Finally, the final fused image is obtained by using inverse NSST to all fused sub-images. In order to verify the effectiveness of the proposed technique, several current popular ones are compared over three different publicly available image sets using four evaluation metrics, and the experimental results demonstrate that the proposed technique performs better in both subjective and objective qualities.     

Double random-phase encryption based on discrete quaternion fourier-transforms

In this paper, a new method for color-image encryption using Discrete Quaternion Fourier-Transforms (DQFT) combined with double random-phase encryption is reported, by which color images can be processed as single channel architecture, instead of as separated color components in three channels, so that the complexity of the encryption system can be effectively reduced without any reduction of its security. The principle of both encryption and decryption is detailed and the noise robustness of the system has been examined experimentally.