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Amidst the rapid advancements in technology, there is a growing demand for processing an increasing volume and quality of images, which necessitates faster image processing capabilities. Enhancing the efficiency of image processing algorithms has thus become a critical priority. Existing quantum image edge detection algorithms tend to exhibit high circuit complexity, which is directly linked to the dimensions of the images being processed, leading to less than optimal computational velocities. In this study, we introduce a quantum image edge detection algorithm that is based on the Laplacian of Gaussian operator. This novel algorithm capitalizes on the quantum parallelism of quantum computing, resulting in a marked enhancement in both the speed and performance of edge detection. To substantiate the practicality of our approach, we conduct simulations using the International Business Machines Quantum (IBM Q) platform. The circuit complexity of our algorithm is meticulously computed, revealing a lower complexity compared to analogous quantum edge detection algorithms. Notably, this complexity is detached from the image size and is solely contingent upon the grayscale value range of the image pixels. [ABSTRACT FROM AUTHOR]

Corners are very important local features for an image that can be widely used in many image processing tasks such as object detection, image recognition and data compression. However, current corner detection algorithms are complex and time consuming. In this paper, a new quantum fast corner detection algorithm is proposed taking full advantage of quantum parallelism. The algorithm is implemented in three steps: the 'nucleus' is defined and its neighborhood pixels are determined in the first step. Two thresholds are selected in the second step, and corners are obtained in the third step. The first and second steps are the same as the classical corner detection algorithm. The third step is divided into two stages. In the first stage, the differences between grayscale values of the nucleus and pixels in the neighborhood are calculated, followed by the comparison of those differences with the 'intensity threshold,' then is the quantum measurement on the comparison results, and finally the measured results are organized into an array. In the second stage, the array is used for counting, followed by the comparison of those counted results with the 'accuracy threshold,' and finally the corners are obtained. It is worth noting that through the quantum–classical–quantum mode, quantum resources can be reduced significantly. The analysis in the proposed quantum fast corner detection algorithm shows that the time complexity and quantum delay of the algorithm do not increase with the increase in the size and number of images, and its time complexity is exponentially lower than that of the classical fast corner detection algorithm. [ABSTRACT FROM AUTHOR]

Median filters are widely used to smooth data in image processing tasks and eliminate noise with extremely large or small magnitude. Thus, this paper proposes a median filtering method for quantum color images in the spatial domain, with the corresponding quantum circuit simulated on IBM the Quantum Experience (IBM Q) platform. In our technique, first, we improve the quantum color image storage module and adopt nine quantum color images to represent the core image (image to be processed) and its 8-neighborhoods. Then, a quantum comparator and a quantum control swap operation with low time complexity are employed to design the quantum median calculation module. Finally, we design the quantum circuit for color image median filtering, simulated on the IBM Q platform. Compared with existing methods, the proposed method's time complexity is significantly reduced from O (q 2) to O(q) and the auxiliary qubit is reduced from O(q) to O(1). The developed scheme provides the framework for encoding the neighborhood information of pixels in supplementary images and reduces the auxiliary qubits and time complexity, demonstrating the potential of quantum image processing for highly efficient image and video processing tasks in the big data era. [ABSTRACT FROM AUTHOR]

Efficient quantum circuits of algebraic operations are vital for quantum algorithms. In this paper, we propose a novel fault-tolerant quantum divider based on long division algorithm using Clifford+T gates. Firstly, two efficient quantum subtractors are designed which we call them equal-bit subtractor and unequal-bit subtractor. The advantage of these quantum subtractors is that the number of the constant inputs is 2, which will dramatically reduce the qubit cost. Then, based on the quantum comparator and the quantum subtractors, we propose a novel fault-tolerant quantum divider. Compared with existing work, the proposed quantum divider has better performances in quantum cost, T-depth, T-count, qubit cost, constant inputs and garbage outputs. Finally, we simulate these algorithms on IBM Quantum Experience (IBM Q Experience) platform, and the probability histograms show that these algorithms are feasible and efficient. [ABSTRACT FROM AUTHOR]

Various quantum computing simulation platforms have developed rapidly in the last 3 years. However, few quantum image processing algorithms are simulated in these platforms. In this paper, we design a dual-threshold quantum image segmentation algorithm and simulate it in IBM Q Experience platform through Qiskit extension. The NEQR quantum image representation model is firstly optimized and simulated, which is found that the number of the auxiliary qubits will not increase as the image's size increases. Then, an efficient quantum comparator to realize the comparison of two numbers is designed. And finally, the high parallelism image segmentation algorithm is proposed and simulated. Suppose the size of an image is 2 n × 2 n and the gray-scale scope is [0, 2 q - 1 ], the time complexity analysis for the quantum image segmentation algorithm shows that the number of basic quantum gate required is proportional to q and will not increase as image's size increases. Thus, the proposed quantum segmentation algorithm is highly parallelism and has polynomial time complexity. In addition, the simulation part of this paper will provide reference for other quantum image processing algorithms. [ABSTRACT FROM AUTHOR]

Quantum image processing has been developing rapidly in recent years. In this paper, the edge detection algorithm for quantum images is proposed. Three steps are consisted: quantum image smoothing, finding the highlight gradient and quantum edge tracking. In order to implement the above processes, a quantum comparator is designed and quantum circuits for all the steps are given. The proposed quantum edge detection algorithm makes full use of quantum parallelism. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Quantum morphology operations are proposed based on the novel enhanced quantum representation model. Two kinds of quantum morphology operations are included: quantum binary and grayscale morphology operations. Dilation and erosion operations are fundamental to morphological operations. Consequently, we focus on quantum binary and flat grayscale dilation and erosion operations and their corresponding circuits. As the basis of designing of binary morphology operations, three basic quantum logic operations AND, OR, and NOT involving two binary images are presented. Thus, quantum binary dilation and erosion operations can be realized based on these logic operations supplemented by quantum measurement operations. As to the design of flat grayscale dilation and erosion operations, the searching for maxima or minima in a certain space is involved; here, we use Grover's search algorithm to get these maxima and minima. With respect that the grayscale is represented by quantum bit string, the quantum bit string comparator is used as an oracle in Grover's search algorithm. In these quantum morphology operations, quantum parallelism is well utilized. The time complexity analysis shows that quantum morphology operations' time complexity is much lower or equal to the classical morphology operations. [ABSTRACT FROM AUTHOR]

A simple quantum representation (SQR) of infrared images is proposed based on the characteristic that infrared images reflect infrared radiation energy of objects. The proposed SQR model is inspired from the Qubit Lattice representation for color images. Instead of the angle parameter of a qubit to store a color as in Qubit Lattice representation, probability of projection measurement is used to store the radiation energy value of each pixel for the first time in this model. Since the relationship between radiation energy values and probability values can be quantified for the limited radiation energy values, it makes the proposed model more clear. In the process of image preparation, only simple quantum gates are used, and the performance comparison with the latest flexible representation of quantum images reveals that SQR can achieve a quadratic speedup in quantum image preparation. Meanwhile, quantum infrared image operations can be performed conveniently based on SQR, including both the global operations and local operations. This paper provides a basic way to express infrared images in quantum computer. [ABSTRACT FROM AUTHOR]

In this study, real-time PCR and immunohistochemistry were used to detect coxsakie and adenovirus receptor (CAR) expression. Both localization and quantity were evaluated in the uteri obtained at days post coitus (dpc) 2.5, 4.5, 6.5, 8.5. Outcome of PCR was assessed by 2 method. Image Pro-Plus 6.0 software was used for quantifying mean density of CAR expression in immunohistochemical sections. We found relatively weak CAR expression in the mouse uteri during implantation window. PCR and immunohistochemistry revealed highest CAR expression was detected on dpc 2.5 followed by down-regulation of CAR at dpc 4.5 and 6.5 (with significant difference). At dpc 8.5, CAR expression was increased slightly again. It is concluded that during implantation, the expression of CAR mRNA and protein is declined, resulting in the impairment of tight junction between cavity epithelium cells. After implantation window closure, CAR appears again to maintain epithelium stability. CAR might play an important role during embryo implantation procedure. [ABSTRACT FROM AUTHOR]

A numerical model for the study of the regional acid precipitation is developed. The model consists of five parts: the distribution patterns of SO concentration, the mesoscale flow fields, the parameterization of SO transformation into SO, the parameterization of precipitation scavenging process, and the relationship between SO content in precipitation and ground level concentration of SO in the air. The distribution of SO, SO and pH for all precipitations in Chongqing area during the period of July to October 1982 are simulated with the model. A comparison of the simulated results with experimental data shows that high SO concentration centres correspond to low pH centres. The source of the acid rain in Chongqing area is local air pollution which is due to the lower effective stack height, low wind velocity in the area, basin topography, and the use of coal with high sulphur content. The mechanism for the formation of the acid precipitation here may be different from that in the United States of America and the Western Europe, where acid rain appears in the area far from pollution source. [ABSTRACT FROM AUTHOR]