Multimodality Image Fusion Based on Quantum Wavelet Transform and Sum-Modified-Laplacian Rule

The parallelism and entanglement characteristics of quantum computation greatly improve the efficiency of image processing tasks. With the sharp increase of data size and requirement of real-time processing in image fusion application, rapid implementation using quantum computation will become the inexorable trend. A novel multimodality image fusion algorithm based on quantum wavelet transform (QWT) and proposed quantum version of sum-modified-laplacian (SML) rule is designed in this paper. The source digital images are firstly represented by flexible representation of quantum image (FRQI) model, and then the quantum form images are transformed with QWT to capture salient features of source images. The quantum version of SML rule is proposed to fuse wavelet coefficients, which has higher efficiency and runs faster than its classical counterpart. The final fused image is obtained by using inverse quantum wavelet transform. The simulations and theoretical analysis verify that the proposed algorithm is effective in the fusion of multimodality images.

     

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.     

Quantum Circuit Realization of Morphological Gradient for Quantum Grayscale Image

In this paper, based on the principle of classical morphology operations, the flat grayscale dilation and erosion operations are proposed for NEQR quantum image model. Furthermore, through combining these two morphology operations, we further realize the morphological gradient operation. As the basis of designing of grayscale morphology operations, a series of quantum circuit designs arepresented, which includes special add one operation UA₁(n) and special subtract one operation US₁(n) both for an n-length qubits sequence, quantum unitary operation U_C, parallel subtractor (PS) module, quantum comparator output the large QCOL and quantum comparator output the small QCOS modules. When designsthe concrete quantum circuit, a sequence of UA₁(n) and US₁(n) modules are used to obtain the quantum image sets based on the shape of specific structuring element. Then, the searching for maximaor minima in a certain space is involved, which can be solved by cascading a series of QCOL and QCOS modules in certain order. Finally, the PS module can be used to calculate the difference of the maxima and minima for producing the morphological gradient. The circuit’s complexity analysis illustrate that our scheme is very lower to the classical morphology operations.

     

一种新的激光雷达距离像噪声抑制方法

主要针对激光雷达距离像的距离反常噪声抑制问题,阐述了激光雷达距离像的噪声原理,分析了应用传统中值滤波方法抑制距离反常噪声的缺陷,提出了基于包围准则的自适应中值滤波算法。该方法首先根据包围准则检测噪声,对5×5滤波窗口内的像素值进行排序差分;然后选择低于门限长度最长的连续差分值对应的像素值作为距离正常值;最后运用中值滤波和加权均值滤波进行噪声抑制。实验结果表明,该方法有效抑制了距离反常噪声,且较好地保护了距离图像中目标的边缘细节,均方根误差分别比3×3和5×5窗口中值滤波法减少了27.1%和9.1%。     

A Novel Image Encryption Scheme Based on Walsh Compressed Quantum Spinning Chaotic Lorenz System

In today’s era, a fascinating discipline is immensely influencing a wide miscellany in different fields of science and technology known as quantum cryptography. The amalgamation of different unconventional themes of information security and fast computing have appended inventiveness and creativity into the performance of quantum systems which exhibits astonishing outcomes surprisingly for the most complicated nonlinear models. The exploitation of chaos theory at quantum scale is a dynamical new approach towards the system of information security. Regarding this a novel image encryption approach based on modern standards of chaos, fast computing and quantum encryption has been proposed in this article. In the designed scheme, Walsh transformation is exploited to get standard image compression as to reduce data being processed resulting in fast computing. Quantum spinning and rotation operators leading new protocols, compressed data is encrypted using quantum spinning and rotation operators. For adding more confusion capability in contemplated algorithm discrete fractional chaotic Lorenz system is also accomplished. The proposed system has been validated through statistical analysis, the assessments accordingly by statistical analysis tests clearly emphasis that proposed scheme of encryption is comparatively equitable for the digital images security.

     

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 Realization of Arnold Scrambling for IFRQI

This paper is concerned with the feasibility of the Arnold scrambling based on Improved Flexible Representation of Quantum Images (IFRQI). Firstly, the flexible representation of quantum image is updated to the improved flexible representation of quantum image (IFRQI) to represent a quantum image with arbitrary size L × B. Then, by making use of Control-NOT gate and Adder-Modular operation, the concrete quantum circuit of Arnold scrambling for IFRQI is designed. Simulation results show the effectiveness of the proposed circuit.     

稀疏孔径光学系统成像恢复算法研究

分析了传统小波阈值去噪方法,给出改进去噪算法.先对稀疏孔径光学系统含噪成像,通过改进小波阈值去噪,提高信噪比,最大程度得到较为理想成像结果,参考修正维纳滤波方法,对去噪结果经过修正维纳滤波实现成像恢复.在实验中,考虑结构非冗余性,利用光学设计软件ZEMAX设计Golay6结构不同填充因子的稀疏孔径光学系统,以本算法进行成像恢复.实验结果表明本文算法优于单独使用维纳滤波或修正维纳滤波方法.     

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.     

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.     

Minimum spanning tree-based random-valued impulse noise detection for a switching median filter

A switching median filter is effective for impulse noise elimination while preserving edges and details of an image. In the switching median filter an impulse noise detector is employed before filtering, and the detection result is used to control whether a pixel should be filtered or not. However, the conventional impulse detector tends to misjudge noise-free pixels constructing line structures to be the noises. We propose a new random-valued impulse noise detector based on the minimum spanning tree, and it is applied to the switching median filtering to eliminate the impulse noise effectively even for the image including line structures. Through the experiments, the effectiveness of the proposed random-valued impulse noise detector is illustrated.     

Quantum Image Encryption Based on Henon Mapping

Quantum image processing has great significance as a branch of quantum computing. This paper gives a quantum image encryption based on Henon mapping, which breaks away from the restriction of classical computers and does the work in quantum computers end to end, including the generation of the chaos sequence, the encryption and the decryption. The algorithm is based on the GQIR quantum image representation model and the two-dimensional Henon chaotic mapping. However, the decimal sequence generated by Henon mapping can not be directly applied to quantum computers. Hence, we reform the Henon mapping by binary shift. The quantum image is encrypted by being XORed with the quantum Henon mapping. Simulation experiments indicate that the encrypted image has good radomness and the pixel values are evenly distributed. Since the chaotic sequence itself is suitable for image encryption, coupled with its own quantum confidentiality, the encryption method of this paper is safe, convenient and reliable.

     

同态技术在红外图像处理中的应用

针对噪声引起的红外图像对比度的下降,采用同态滤波增强方法对红外图像进行校正,减小了噪声的影响,提高了原图像的对比度和像质,为进一步提取图像中的特征信息打下了良好的基础.通过计算机模拟分析和验证,证实了同态滤波是红外图像对比度增强的有效预处理方法.     

A Secret Sharing Scheme for Quantum Gray and Color Images Based on Encryption

This paper is concerned with the better security of quantum image secret sharing (QISS) algorithm. The improved QISS (IQISS) scheme is implemented on both quantum gray image (FRQI) and quantum color image (MCQI). The new IQISS scheme comprises efficient sharing process and recovering process. The core idea of the sharing process is to combine encryption and measurement for two types of quantum secret images to acquire the quantum shadow images. In the recovering process, strip operation is firstly utilized on the shadow images. Afterwards, the decryption algorithm is used to recover the original quantum secret image. Experiments demonstrate that significant improvements in the security are in favor of the proposed approach.

     

Implementation of Fault-Tolerant Encoding Circuit Based on Stabilizer Implementation and “Flag” Bits in Steane Code

Quantum error correction (QEC) is an effective way to overcome quantum noise and de-coherence, meanwhile the fault tolerance of the encoding circuit, syndrome measurement circuit, and logical gate realization circuit must be ensured so as to achieve reliable quantum computing. Steane code is one of the most famous codes, proposed in 1996, however, the classical encoding circuit based on stabilizer implementation is not fault-tolerant. In this paper, we propose a method to design a fault-tolerant encoding circuit for Calderbank-Shor-Steane (CSS) code based on stabilizer implementation and “flag” bits. We use the Steane code as an example to depict in detail the fault-tolerant encoding circuit design process including the logical operation implementation, the stabilizer implementation, and the “flag” qubits design. The simulation results show that assuming only one quantum gate will be wrong with a certain probability p, the classical encoding circuit will have logic errors proportional to p; our proposed circuit is fault-tolerant as with the help of the “flag” bits, all types of errors in the encoding process can be accurately and uniquely determined, the errors can be fixed. If all the gates will be wrong with a certain probability p, which is the actual situation, the proposed encoding circuit will also be wrong with a certain probability, but its error rate has been reduced greatly from p to p2 compared with the original circuit. This encoding circuit design process can be extended to other CSS codes to improve the correctness of the encoding circuit.     

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.     

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.     

红外图像空间噪声分析和预处理方法改进

讨论了基于天空背景下红外图像空间噪声组成，分析了各种空间噪声的形成原因,给出了红外图像噪声类型及其对图像质量的不同影响。分析结果认为红外图像空间噪声主要为高斯噪声和椒盐噪声。综合考虑均值法和中值法在处理噪声类型上的不同，提出了一种基于简单阈值的改进型中值法。对于不同的阈值，其算子有所不同。而M×N处理区域内每一点算子的权值取决于区域灰度中值，该点灰度越接近中值权值越大。处理后的图像背景更平滑，信噪比和改进的峰值信噪比提高了1.2dB以上，表明改进型中值法优于传统滤波方法。     

Switching non-local vector median filter

This paper describes a novel image filtering method that removes random-valued impulse noise superimposed on a natural color image. In impulse noise removal, it is essential to employ a switching-type filtering method, as used in the well-known switching median filter, to preserve the detail of an original image with good quality. In color image filtering, it is generally preferable to deal with the red (R), green (G), and blue (B) components of each pixel of a color image as elements of a vectorized signal, as in the well-known vector median filter, rather than as component-wise signals to prevent a color shift after filtering. By taking these fundamentals into consideration, we propose a switching-type vector median filter with non-local processing that mainly consists of a noise detector and a noise removal filter. Concretely, we propose a noise detector that proactively detects noise-corrupted pixels by focusing attention on the isolation tendencies of pixels of interest not in an input image but in difference images between RGB components. Furthermore, as the noise removal filter, we propose an extended version of the non-local median filter, we proposed previously for grayscale image processing, named the non-local vector median filter, which is designed for color image processing. The proposed method realizes a superior balance between the preservation of detail and impulse noise removal by proactive noise detection and non-local switching vector median filtering, respectively. The effectiveness and validity of the proposed method are verified in a series of experiments using natural color images.     

Improved Handwritten Digit Recognition using Quantum K-Nearest Neighbor Algorithm

Handwritten numeral recognition is a technology for automatic recognition and classification of handwritten numeral input through machine learning model. This is widely used in postal code digital automatic system to sort letters. The classical k-nearest neighbor algorithm is used in the traditional digital recognition training model. The recognized digital image classification is obtained through similarity measure or calculation and K value selection. Nonetheless, as the applied data volume exceeds a certain threshold, the time complexity of the model increases exponentially upon the similarity measure and K value search. This condition makes it hard to apply the model universally. In this paper, we introduce quantum computing, that is where digital image information is stored in the quantum state, and its similarity is calculated in parallel. Also, the most similar K points are obtained through the Grover algorithm. The theoretical analysis of the proposed improved algorithm shows that, handwritten numeral recognition based on quantum k-neighbor algorithm can improved upon time complexity of \( \mathrm{O}\left(\mathrm{R}\sqrt{kM}\right) \) of the existing algorithm.