Quantum Image Steganography and Steganalysis Based On LSQu-Blocks Image Information Concealing Algorithm

Quantum steganography can solve some problems that are considered inefficient in image information concealing. It researches on Quantum image information concealing to have been widely exploited in recent years. Quantum image information concealing can be categorized into quantum image digital blocking, quantum image stereography, anonymity and other branches. Least significant bit (LSB) information concealing plays vital roles in the classical world because many image information concealing algorithms are designed based on it. Firstly, based on the novel enhanced quantum representation (NEQR), image uniform blocks clustering around the concrete the least significant Qu-block (LSQB) information concealing algorithm for quantum image steganography is presented. Secondly, a clustering algorithm is proposed to optimize the concealment of important data. Finally, we used Con-Steg algorithm to conceal the clustered image blocks. Information concealing located on the Fourier domain of an image can achieve the security of image information, thus we further discuss the Fourier domain LSQu-block information concealing algorithm for quantum image based on Quantum Fourier Transforms. In our algorithms, the corresponding unitary Transformations are designed to realize the aim of concealing the secret information to the least significant Qu-block representing color of the quantum cover image. Finally, the procedures of extracting the secret information are illustrated. Quantum image LSQu-block image information concealing algorithm can be applied in many fields according to different needs.     

A high capacity image steganography using multi-layer embedding

Until today, high-capacity is still one of important research parts in information hiding. After the secret information embedded is extracted, the demand for the image reversibility for the total recovery of the original object without any distortion goes high. This paper proposes a high capacity steganography using multilayer embedding (CRS), which can enhance the performance of information hiding system. The experimental results show the proposed CRS scheme has better performance than others. Moreover, the proposed CRS method can display the advantages of good quality image and low complexity of computation.     

基于小波分解系数的贝叶斯人脸识别方法

本文给出了贝叶斯人脸识别方法中匹配准则的多个近似表达式及一种实用的快速计算方法,在此基础上,利用反对称双正交小波变换的微分算子功能,提出了一种利用两幅人脸图像的小波变换系数差作为模式矢量的贝叶斯人脸识别方法,并利用AR人脸图象库进行了实验,实验结果表明本文方法与基于图像灰度的类似方法相比,识别率提高8％左右,此外本文方法也提供了一条在图像压缩数据域中实现人脸识别的可能途径。     

基于小波分解系数的贝叶斯人脸识别方法

本文给出了贝叶斯人脸识别方法中匹配准则的多个近似表达式及一种实用的快速计算方法.在此基础上,利用反对称双正交小波变换的微分算子功能,提出了一种利用两幅人脸图象的小波变换系数差作为模式矢量的贝叶斯人脸识别方法,并利用AR人脸图象库进行了实验.实验结果表明本文方法与基于图象灰度的类似方法相比,识别率提高8%左右.此外本文方法也提供了一条在图象压缩数据域中实现人脸识别的可能途径.     

Linear exchanging operation and random phase encoding in gyrator transform domain for double image encryption

We present an optical method for double image encryption by using linear exchanging operation and double random phase encoding (DRPE) in the gyrator transform (GT) domain. In the linear exchanging operation, two primitive images are linearly recombined via a random orthogonal transform matrix. The resultant blended images are employed to constitute a complex-valued image, which is then encoded into a noise-like encrypted image by a DRPE structure in the GT domain. One can recover the primitive images exactly with all decryption keys correctly applied, including the transform orders, the random phase masks and random angle function used for linear exchanging operation. Computer simulations have been given to demonstrate that the proposed scheme eliminates the difference in key spaces between the phase-based image and the amplitude-based image encountered in the previous schemes. Moreover, our scheme has considerably high security level and certain robustness against data loss and noise disturbance.     

A robust steganographic technique based on improved chaotic-range systems

This paper presents a novel chaos-based technique of steganography in spatial domain. In the last decade, chaos theory has gained utmost importance in multimedia security applications. Generally, 1-D chaotic maps are employed because of computational ease and structural simplicity but their limited chaotic range is an obstacle. In the proposed work, we model the nonlinear combinations of 1-D chaotic maps. These chaotic systems possess chaotic behavior throughout the domain. We, for the first time, propose an effective application of these improved chaotic systems in steganography. These newly synthesized systems are used to embed secret information in the least significant bits (LSBs) of the host image. By comparing with some recent models, we prove that involving improved chaotic systems in steganographic approach really produces extraordinary outcomes. We determine the strength of our steganographic algorithm through the most significant statistical analyses such as information entropy, correlation, contrast, energy, homogeneity, peak signal to noise ratio (PSNR) and mean square error (MSE). We further prove the robustness of the anticipated technique against several image processing attacks. The upshot of these analysis techniques shows that our algorithm is highly reliable and produces coherent results.     

Double image encryption based on linear blend operation and random phase encoding in fractional Fourier domain

A novel method for double image encryption is proposed by using linear blend operation and double-random phase encoding (DRPE) in the fractional Fourier domain. In the linear blend operation, a random orthogonal matrix is defined to linearly recombined pixel values of two original images. The resultant blended images are employed to constitute a complex-valued image, which is encrypted into an encrypted image with stationary white distribution by the DRPE in the fractional Fourier domain. The primitive images can be exactly recovered by applying correct keys with fractional orders, random phase masks and random angle function that is used in linear blend operation. Numerical simulations demonstrate that the proposed scheme has considerably high security level and certain robustness against data loss and noise disturbance.     

基于字典学习的稠密光场重建算法

相机阵列是获取空间中目标光场信息的重要手段,采用大规模密集相机阵列获取高角度分辨率光场的方法增加了采样难度和设备成本,同时产生的大量数据的同步和传输需求也限制了光场采样规模.为了实现稀疏光场采样的稠密重建,本文基于稀疏光场数据,分析同一场景多视角图像的空间、角度信息的关联性和冗余性,建立有效的光场字典学习和稀疏编码数学模型,并根据稀疏编码元素间的约束关系,建立虚拟角度图像稀疏编码恢复模型,提出变换域稀疏编码恢复方法,并结合多场景稠密重建实验,验证提出方法的有效性.实验结果表明,本文方法能够对场景中的遮挡、阴影以及复杂的光影变化信息进行高质量恢复,可以用于复杂场景的稀疏光场稠密重建.本研究实现了线性采集稀疏光场的稠密重建,未来将针对非线性采集稀疏光场的稠密重建进行研究,以推进光场成像在实际工程中的应用.     

Steganography and authentication in image sharing without parity bits

Recently, a polynomial-based (k, n) steganography and authenticated image sharing (SAIS) scheme was proposed to share a secret image into n stego-images. At the same time, one can reconstruct a secret image with any k or more than k stego-images, but one cannot obtain any information about the secret from fewer than k stego-images. The beauty of a (k, n)-SAIS scheme is that it provides the threshold property (i.e., k is the threshold value), the steganography (i.e., stego-images look like cover images), and authentication (i.e., detection of manipulated stego-images). All existing SAIS schemes require parity bits for authentication. In this paper, we present a novel approach without needing parity bits. In addition, our (k, n)-SAIS scheme provides better visual quality and has higher detection ratio with respect to all previous (k, n)-SAIS schemes.     

Nuclear norm-regularized SENSE reconstruction

SENSitivity Encoding (SENSE) is a mathematically optimal parallel magnetic resonance (MRI) imaging technique when the coil sensitivities are known. In recent times, compressed sensing (CS)-based techniques are incorporated within the SENSE reconstruction framework to recover the underlying MR image. CS-based techniques exploit the fact that the MR images are sparse in a transform domain (e.g., wavelets). Mathematically, this leads to an l(1)-norm-regularized SENSE reconstruction. In this work, we show that instead of reconstructing the image by exploiting its transform domain sparsity, we can exploit its rank deficiency to reconstruct it. This leads to a nuclear norm-regularized SENSE problem. The reconstruction accuracy from our proposed method is the same as the l(1)-norm-regularized SENSE, but the advantage of our method is that it is about an order of magnitude faster.     

A unified classifier for robust face recognition based on combining multiple subspace algorithms

Face recognition being the fastest growing biometric technology has expanded manifold in the last few years. Various new algorithms and commercial systems have been proposed and developed. However, none of the proposed or developed algorithm is a complete solution because it may work very well on one set of images with say illumination changes but may not work properly on another set of image variations like expression variations. This study is motivated by the fact that any single classifier cannot claim to show generally better performance against all facial image variations. To overcome this shortcoming and achieve generality, combining several classifiers using various strategies has been studied extensively also incorporating the question of suitability of any classifier for this task. The study is based on the outcome of a comprehensive comparative analysis conducted on a combination of six subspace extraction algorithms and four distance metrics on three facial databases. The analysis leads to the selection of the most suitable classifiers which performs better on one task or the other. These classifiers are then combined together onto an ensemble classifier by two different strategies of weighted sum and re-ranking. The results of the ensemble classifier show that these strategies can be effectively used to construct a single classifier that can successfully handle varying facial image conditions of illumination, aging and facial expressions.     

结合视觉注意力机制和图像锐度的无参图像质量评价方法

针对模糊图像的质量评价,提出一种新的无参图像质量评价方法,该方法结合了自底向上的视觉注意力机制和自顶向下的图像锐度评价标准。根据人眼视觉注意力机制模型,分别计算颜色、亮度和方向显著度图像,通过竞争机制得到人眼优先关注的区域; 利用无参图像锐度评价方法分别对优先关注的区域及背景区域进行评价,综合2个区域的评价结果得到最终的图像质量评价指标。利用该方法分别对相向运动过程中所产生的模糊图像和图像质量评价Live数据库中的高斯模糊图像进行了评价,结果表明:针对两类图像的评价结果与主观评价结果的相关系数均较高,其中,针对相向运动模糊图像的主客观评价结果的相关系数达到0.98。该方法能够胜任对模糊图像的客观质量评价。     

Noise removal in three-fringe photoelasticity by median filtering

In three-fringe photoelasticity, the total fringe order is obtained by comparing the colour of the unknown photoelastic fringe pattern with a calibration specimen. Comparison is conventionally done by minimising the colour difference error using the least square method. This can give the total fringe order from a single-colour isochromatic field. This technique, however, leads to misidentification of fringe order in some regions. Some researches have proposed boundary identification technique to remove this error. Whilst this ensures continuity of fringe order data over the domain, this does not provide accurate results for every region. A simpler method has been proposed in this paper using median filtering which can be easily undertaken using any standard image processing software. The method is demonstrated with the help of a simple example of disc under diametral compression.     

脊小波变换域模糊自适应图像增强算法

提出了基于脊小波(ridgelet)变换域的模糊自适应图像增强算法,利用脊小波变换在表示图像线性奇异边缘时具有独特的优越性,达到突出边缘和抑制噪声的目的。利用频域内傅里叶投影变换定理,提出优化有限拉东(Radon)变换系数顺序的方法,使得拉东变换后图像的折回现象得到改善;利用广义模糊集合概念和最大模糊熵原理,提出一种自适应设置模糊增强函数方法,使得增强后的图像在抑制噪声、增强特征方面达到较好折衷。通过模拟实验显示,该算法优于传统的增强方式,在低信噪比情况(2.5~5.5 dB)下,其边缘检测概率大于二维小波增强方式约50%。应用于含有局部线形裂纹的路面病害图像的增强,可以将裂纹信号基本增强出来,且对路面上离散的油滴、石子等点噪声抑制较好。     

A two-phase face recognition method in frequency domain

In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images.     

基于人眼视觉系统的假彩色融合图像质量的评价方法

随着图像融合技术的发展,各种融合算法层出不穷,而很多情况下最终的融合图像是由人眼观察的,因此基于人眼视觉系统的图像融合质量评价显得尤为重要.为了能够模拟人眼对于融合图像的感知,得到融合后图像质量的客观评价,本文提出了一种基于色差理论的假彩色融合图像质量的评价方法.首先将源图像和融合图像转化到CIE L*a*b*均匀色空间,在频域对图像进行对比度敏感函数滤波,通过计算滤波后融合图像的色差判断图像的细节信息,在一定程度上色差越大信息越丰富;通过计算融合图像与源图像的色差判断融合图像与源图像的相关性,相关性越高,融合算法越好.通过融合图像的色差大小以及与源图像的相关性两个参量,得出融合算法的优劣.实验表明,与其他评价方法相比,本文提出的评价方法与人眼观察的结果较为一致.     

基于指导滤波的图像盲复原算法

作为图像处理领域的重要分支和研究热点之一, 图像复原方法 的研究始终具有重要理论意义和广泛的应用价值, 图像盲复原一直以来都 是图像复原中比较困难的问题之一. 针对相机与所拍摄景物之间由于相对 位置移动而使所获得图像发生运动模糊的情况, 本文提出了一种基于指导滤波 的图像盲复原算法. 我们首先通过频域迭代算法对点扩散函数 进行估计. 然后, 由于指导滤波具有较好的保持图像边缘的特性, 我们应用基于指导滤波的图像非盲复原算法恢复目标图像. 对以上两步进行反复迭代, 直到获得最终的清晰图像. 为了验证本文所提算法的有效性, 给出了多组对比实验. 实验结果表明, 本文所提算法能够在有效地抑制噪声和振铃 效应的同时, 还能够更好的保持图像的边缘和纹理细节. 因此, 本文算法可以获得更高质量的复原图像.     

小波包特征提取及方差相似度的人脸识别

小波包变换是小波变换的推广,可视为普通小波函数的线性组合,具有良好的时频局部性和正交性,随着分解层数的增加,小波包分解能够在所有的频率范围聚焦。利用图像小波包变换的系数矩阵,能够构造出不同的人脸特征向量。针对人脸识别过程中的图像匹配问题,采用计算人脸特征向量方差的方法,并通过方差与权值的对应关系,转换出用于相似度计算的权值。基于理论推导得到的权值具有很好的稳定性,由这些权值计算出的方差相似度也具有较强的适应性,能够减弱由图像噪声、变形等干扰带来的影响。实验表明,该方法识别率高、实时性好。     

IKWI-net: A cross-domain convolutional neural network for undersampled magnetic resonance image reconstruction

Magnetic resonance imaging (MRI) is widely used to get the information of anatomical structure and physiological function with the advantages of high resolution and non-invasive scanning. But the long acquisition time limits its application. To reduce the time consumption of MRI, compressed sensing (CS) theory has been proposed to reconstruct MRI images from undersampled k-space data. But conventional CS methods mostly use iterative methods that take lots of time. Recently, deep learning methods are proposed to achieve faster reconstruction, but most of them only pay attention to a single domain, such as the image domain or k-space. To take advantage of the feature representation in different domains, we propose a cross-domain method based on deep learning, which first uses convolutional neural networks (CNNs) in the image domain, k-space and wavelet domain simultaneously. The combined order of the three domains is also first studied in this work, which has a significant effect on reconstruction. The proposed IKWI-net achieves the best performance in various combinations, which utilizes CNNs in the image domain, k-space, wavelet domain and image domain sequentially. Compared with several deep learning methods, experiments show it also achieves mean improvements of 0.91 dB in peak signal-to-noise ratio (PSNR) and 0.005 in structural similarity (SSIM).     

一种自适应线性透射率估计去雾算法

为了有效复原雾霾天气下退化的图像, 文章提出了一种自适应线性透射率估计去雾算法。建立有雾图像与无雾图像最小值通道之间的线性变换模型; 利用有雾图像的混合通道得到自适应参数, 结合自适应参数和线性变换模型估计出透射率, 通过有雾图像的最小值通道构造高斯函数来补偿估计明亮区域透射率, 提升该区域透射率的准确度, 再使用交叉双边滤波器消除纹理效应得到优化透射率; 最后, 结合大气散射模型复原出无雾图像。实验结果表明, 该方法有效降低了时间复杂度, 且复原的图像细节明显, 明亮度适宜。