Improved Quantum Image Filtering in the Spatial Domain

We investigate the quantum image filtering in spatial domain proposed by Yuan et al. (Int. J. Theor. Phys. 56(8), 2495–2511, 2017). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be a defect in it: the quantum multiplication was replaced by quantum addition. There are two shortcomings: 1) We should know exactly the value of the filter coefficients before each filtering behavior. 2) This method is only suitable for integer filter coefficients but not for decimal filter coefficients. In this paper, an improved version is proposed which takes full advantage of the quantum multiplication and can overcome these two shortcomings. The theoretical analysis indicates that the time complexity is the same as the previous algorithm. So this algorithm is also efficient.     

Quantum Image Filtering in the Spatial Domain

Quantum image processing has developed rapidly in recent years. In this paper, we propose a framework of quantum image filtering in the spatial domain. We proved that a high quantum parallel method to correlate the image and the filter mask can be achieved, even though the quantum correlation of two sequences is physically impossible. In order to avoid this impossible, we use quantum addition operation instead of quantum multiplication. We provide the quantum circuit that can realize the filtering task and present several simulation results on grayscale images. The main advantage of the quantum version lies in the efficient correlation between the quantum image and the filter mask.     

小波域高斯混合模型与中值滤波的混合图像去噪研究

基于高斯混合模型的小波去噪方法并结合中值滤波法对脉冲噪音有较好滤除效果的特点,将这两种方法结合起来,对含有高斯脉冲混合噪音图像进行去噪处理.该算法采用Matlab语言进行仿真.实验结果表明,这种混合去噪方法的效果要优于单纯使用中值滤波或者小波去噪的效果.     

一种改进的自适应中值滤波算法

针对椒盐噪声的特点,提出了一种改进的自适应中值滤波算法。该算法通过对小窗口内非噪声点的检测来决定是增大滤波窗口还是选择输出。新算法尽可能地减小了滤波窗口,使得图像细节得到更好的保持。数值试验结果表明,新算法能够在有效抑制噪声的同时更好地保持图像细节信息,尤其在高概率密度噪声条件（〉70%）下也能取得较好的结果,比传统自...     

基于局域均值滤波的Stoilov改进算法

Stoilov算法中相移量的计算依赖于采集到的变形条纹图的光强,存在对光强的减法、除法和开方等运算,使相位计算时在某些位置出现分子分母为零或开方为复数等奇异现象,导致相位展开无意义或出现超大误差。从而使三维面形重构出现畸变、失真,甚至无法进行三维重构。大量实验说明这些奇异点都是孤立的点,本文提出一种基于局域均值滤波的方法对Stoilov算法进行改进,首先对奇异点进行标记,然后依次用其周围8个点中的非奇异点的平均值代替该奇异点的值。实验结果表明,该方法在考虑传感系统带来的误差的情况下,有效抑制了奇异现象,提高了三维测量精度。     

A Spatial Domain Quantum Watermarking Scheme

This paper presents a spatial domain quantum watermarking scheme. For a quantum watermarking scheme, a feasible quantum circuit is a key to achieve it. This paper gives a feasible quantum circuit for the presented scheme. In order to give the quantum circuit, a new quantum multi-control rotation gate, which can be achieved with quantum basic gates, is designed. With this quantum circuit, our scheme can arbitrarily control the embedding position of watermark images on carrier images with the aid of auxiliary qubits. Besides reversely acting the given quantum circuit, the paper gives another watermark extracting algorithm based on quantum measurements. Moreover, this paper also gives a new quantum image scrambling method and its quantum circuit. Differ from other quantum watermarking schemes, all given quantum circuits can be implemented with basic quantum gates. Moreover, the scheme is a spatial domain watermarking scheme, and is not based on any transform algorithm on quantum images. Meanwhile, it can make sure the watermark be secure even though the watermark has been found. With the given quantum circuit, this paper implements simulation experiments for the presented scheme. The experimental result shows that the scheme does well in the visual quality and the embedding capacity.     

提升小波变换与中值滤波结合的红外光谱消噪

在光谱数据的定量分析中,噪声的存在常常会影响结果的准确性。为提高红外光谱分析精度,需要对光谱数据进行去噪处理。将一种光滑阈值函数和一种分层阈值选取方法应用到提升小波域光谱信号的去噪处理中,并对提升小波重构信号进行中值滤波。对某小麦品种的实测光谱信号,添加信噪比为21.17 dB的噪声后采用该方法进行去噪处理,并利用信噪比(SNR)、均方根误差(RMSE)、峰值平均相对误差(AREPV)以及峰位平均误差(AEPP)四项指标对去噪效果进行评价。结果表明,与软阈值法与硬阈值法相比,该方法能更有效地去除光谱信号中的噪声,保留光谱中的有用信息,提高光谱信噪比,降低均方根误差、峰值平均相对误差以及峰位平均误差,提高光谱的分析能力。     

Quantum Image Edge Extraction Based on Improved Sobel Operator

Edge extraction is a basic task in image processing. This paper proposes a quantum image edge extraction algorithm based on improved sobel operator for the generalized quantum image representation (GQIR) to solve the real-time problem. The quantum image model of GQIR can store arbitrary quantum images with a size of H × W. Our scheme can calculate the gradients of image intensity of all the pixels simultaneously. Then, the concrete circuits of quantum image edge extraction algorithm are implemented by using a series of quantum operators which have been designed. Compared with existing quantum edge extraction algorithms, our scheme can achieve more accurate edge extraction, especially for diagonal edges. Finally, the complexity of the quantum circuits were been analyzed based on the basic quantum gates and give the simulation experiment results on classical computer.

     

Quantum Image Encryption Based on Iterative Framework of Frequency-Spatial Domain Transforms

A novel quantum image encryption and decryption algorithm based on iteration framework of frequency-spatial domain transforms is proposed. In this paper, the images are represented in the flexible representation for quantum images (FRQI). Previous quantum image encryption algorithms are realized by spatial domain transform to scramble the position information of original images and frequency domain transform to encode the color information of images. But there are some problems such as the periodicity of spatial domain transform, which will make it easy to recover the original images. Hence, we present the iterative framework of frequency-spatial domain transforms. Based on the iterative framework, the novel encryption algorithm uses Fibonacci transform and geometric transform for many times to scramble the position information of the original images and double random-phase encoding to encode the color information of the images. The encryption keys include the iterative time t of the Fibonacci transform, the iterative time l of the geometric transform, the geometric transform matrix G i which is n × n matrix, the classical binary sequences K (\(k_{0}k_{1}{\ldots } k_{2^{2n}-1}\)) and \(D(d_{0}d_{1}{\ldots } d_{2^{2n}-1}\)). Here the key space of Fibonacci transform and geometric transform are both estimated to be 2²⁶. The key space of binary sequences is (2 ^n×n ) × (2 ^n×n ). Then the key space of the entire algorithm is about \(2^{2{n^{2}}+52}\). Since all quantum operations are invertible, the quantum image decryption algorithm is the inverse of the encryption algorithm. The results of numerical simulation and analysis indicate that the proposed algorithm has high security and high sensitivity.     

基于运动补偿的红外图像噪音时域IIR滤波算法

从模式识别的角度出发将图像分为运动区域和非运动区域,采用基于噪音分布模型的运动检测技术与基于概率松弛法的运动区域标号技术相结合对这两种区域进行具体划分,对分类后的图像采用时域IIR滤波,不同区域采用不同的影响因子,以期达到最终的运动补偿.仿真结果表明该方法有效抑制了噪音,同时并未引起明显的运动模糊,图像的视觉效果得到了极大改善.     

Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation

Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.     

A Hybrid Domain Image Encryption Algorithm Based on Improved Henon Map

A hybrid domain image encryption algorithm is developed by integrating with improved Henon map, integer wavelet transform (IWT), bit-plane decomposition, and deoxyribonucleic acid (DNA) sequence operations. First, we improve the classical two-dimensional Henon map. The improved Henon map is called 2D-ICHM, and its chaotic performance is analyzed. Compared with some existing chaotic maps, 2D-ICHM has larger parameter space, continuous chaotic range, and more complex dynamic behavior. Second, an image encryption structure based on diffusion–scrambling–diffusion and spatial domain–frequency domain–spatial domain is proposed, which we call the double sandwich structure. In the encryption process, the diffusion and scrambling operations are performed in the spatial and frequency domains, respectively. In addition, initial values and system parameters of the 2D-ICHM are obtained by the secure hash algorithm-512 (SHA-512) hash value of the plain image and the given parameters. Consequently, the proposed algorithm is highly sensitive to plain images. Finally, simulation experiments and security analysis show that the proposed algorithm has a high level of security and strong robustness to various cryptanalytic attacks.     

Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain

Multimodal medical image fusion aims to fuse images with complementary multisource information. In this paper, we propose a novel multimodal medical image fusion method using pulse coupled neural network (PCNN) and a weighted sum of eight-neighborhood-based modified Laplacian (WSEML) integrating guided image filtering (GIF) in non-subsampled contourlet transform (NSCT) domain. Firstly, the source images are decomposed by NSCT, several low- and high-frequency sub-bands are generated. Secondly, the PCNN-based fusion rule is used to process the low-frequency components, and the GIF-WSEML fusion model is used to process the high-frequency components. Finally, the fused image is obtained by integrating the fused low- and high-frequency sub-bands. The experimental results demonstrate that the proposed method can achieve better performance in terms of multimodal medical image fusion. The proposed algorithm also has obvious advantages in objective evaluation indexes VIFF, Q_W, API, SD, EN and time consumption.     

Quantum Image Encryption Based on Quantum Image Decomposition

Aiming at the slow processing speed of classic image encryption algorithms and the security analysis of existing quantum image encryption algorithms, this paper combines the representation method of quantum images and proposes a quantum image encryption algorithm based on image correlation decomposition. Using the principle of quantum state superposition and measurement, the association between image pixels is established, the image is decomposed into a series of feature sub-images and stored in a complete binary tree set, and different sub-images are operated and encrypted by random phase operation and quantum rotation operation. Then superimpose all the sub-images to obtain the ciphertext image. The algorithm has a larger key space so that it can resist brute force attacks. At the same time, the quantum encryption algorithm has lower computational complexity than classic encryption algorithms. In addition, because the ciphertext image is transmitted in the communication channel in the form of a quantum state, the security of quantum image encryption also surpasses the security of classical image encryption.

     

一维光栅的阿贝成像原理及空间滤波的研究

主要探讨了一维矩形光栅的阿贝成像原理及空间滤波问题的研究,分别从实验上对一维光栅在阿贝成像原理实验进行各种空间滤波,从理论上讨论了一维光栅的占空比以及各级衍射波对成像结果的影响情况。实验与理论结果完全符合,有利于深入理解阿贝成像原理及空间滤波。     

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.     

Quantum Image Encryption Algorithm Based on Quantum Image XOR Operations

A novel encryption algorithm for quantum images based on quantum image XOR operations is designed. The quantum image XOR operations are designed by using the hyper-chaotic sequences generated with the Chen’s hyper-chaotic system to control the control-NOT operation, which is used to encode gray-level information. The initial conditions of the Chen’s hyper-chaotic system are the keys, which guarantee the security of the proposed quantum image encryption algorithm. Numerical simulations and theoretical analyses demonstrate that the proposed quantum image encryption algorithm has larger key space, higher key sensitivity, stronger resistance of statistical analysis and lower computational complexity than its classical counterparts.     

Quantum Image Location

Quantum image processing has been a hot topic as a consequence of the development of quantum computation. Many quantum image processing algorithms have been proposed, whose efficiency are theoretically higher than their corresponding classical algorithms. However, most of the quantum schemes do not consider the problem of measurement. If users want to get the results, they must measure the final state many times to get all the pixels’ values. Moreover, executing the algorithm one time, users can only measure the final state one time. In order to measure it many times, users must execute the algorithms many times. If the measurement process is taken into account, whether or not the algorithms are really efficient needs to be reconsidered. In this paper, we try to solve the problem of measurement and give a quantum image location algorithm. This scheme modifies the probability of pixels to make the target pixel to be measured with higher probability. Furthermore, it only has linear complexity.     

基于改进自适应Wiener滤波的光学合成孔径系统图像复原算法

为了解决光学合成孔径系统成像模糊问题以及研究其相应图像复原算法,采用Zemax软件分析了四孔径光学合成孔径系统的MTF,以及其像质模糊原因。并用MATLAB仿真分析常用的Wiener滤波复原算法,得出该算法在处理合成孔径系统图像存在不足。提出了一种基于K值自适应改变Wiener滤波图像复原方法,通过计算机仿真验证表明,该算法可使合成孔径系统降质图像的像质得到较好改善,复原效果优于Wiener滤波,且其像质评价参数PSNR以及ISNR分别提高了6.89%和1.1dB。     

改进的傅里叶域小波域联合去模糊算法

傅里叶域与小波域的联合去模糊算法在低噪声时具有优越的恢复效果,但是这种联合去模糊算法并不适用于含噪声的模糊图像.为了解决这一问题,本文将先验约束分别引入傅里叶域的去模糊步骤和小波域的去噪步骤.在傅里叶域,用矩阵形式表示目标函数.对目标函数添加平滑约束并且通过噪声水平和模糊图像高频信息计算得到平滑约束项的滤波系数.同样方式,在小波域对小波域目标函数添加能量约束,实现小波域目标函数的正则化过程.分析傅里叶域的噪声放大程度,通过傅里叶域的滤波系数计算得到小波域能量约束的滤波系数.傅里叶域的平滑约束可以抑制滤波过程中噪声的产生,小波域的能量约束可以提高小波域滤波的鲁棒性.仿真实验表明,改进的算法相比于原始算法具有更好的鲁棒性,可以有效提高图像的恢复质量.对于噪声标准差为0.010.1的模糊图像,改进算法恢复图像峰值信噪比比原始算法恢复图像的峰值信噪比高1左右.并且改进算法对于高斯型点扩散函数误差具有鲁棒性,当点扩散函数估计方差与实际方差相差0.4时,改进算法的恢复效果仍优于原始算法.