Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.
Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.
Quantum image processing is a system where both quantum computing and image processings are key. Now it is required to investigate how to apply image processing concepts such as image edge detection and improved Sobel operator, to quantum image processing. Just as traditional image processing helps to image manipulation, our efforts in quantum image processing contribute to the further development of quantum algorithm and quantum information theory. Edge detection is an important problem in traditional image processing, and image edge detection algorithm can filter image edge features and retain important attributes. We proposed a quantum edge detection algorithm, which employs improved Sobel operator to improve the performance, so as to solve the problem of unsatisfactory traditional image edge detection methods. In practical application, the amount of image data to be processed increases sharply, and the computing power of classical computer becomes a limitation. Quantum information processing can effectively accelerate many classical problems by virtue of quantum mechanical characteristics, such as quantum superposition, entanglement, parallelism. Our proposed method uses the novel enhanced quantum representation to store quantum images, which stores all the pixels in the image in a superimposed state, realizing parallel computation. The improved eight-direction Sobel operator is used to calculate the gray gradient, and the quantum circuit is designed to realize quantum edge detection. Simulation results have shown that our algorithm can extract edge with dimension 2n× 2nwhen the computational complexity is O(n2+ 2q+3). The improved algorithm can detect more edge details and has strong adaptability.
In this study, we propose quantum representation of multiwavelength images (QRMW) that gives preparation and retrieving procedures of quantum images. The proposed QRMW model represents multichannel and 2n×2m images. Moreover, we present image comparison and some image operations based on the QRMW model. Comparing our model with the models in the literature, the QRMW model has less time complexity. It also uses fewer qubits than existing models in the literature.
This work introduces a novel quantum algorithm for gradient-based edge detection that operates entirely within the quantum circuit model. Grayscale images are encoded using the Novel Enhanced Quantum Representation (NEQR), allowing exact arithmetic on pixel intensities. Directional gradients are computed by generating superpositions of neighboring pixels via cyclic shift operations and performing subtraction with an exact quantum arithmetic circuit. To refine accuracy, we introduce a direction-aware shifting mechanism that aligns edges with the darker side of intensity transitions. Our novel Quantum Partitioning Algorithm enables efficient in-place thresholding of edge candidates. This work exhibits polynomial-time improvements and optimizes the ancilla count compared to previous NEQR-based quantum edge detection algorithms. These results demonstrate a resource-efficient and fully quantum approach to edge detection, highlighting a practical quantum advantage in image processing.
Abstract With the increase in the quantity and quality of images that need to be processed, there are higher requirements for the speed of image processing. Thus, it is necessary to speed-up the image processing algorithms. In this paper, we design a quantum image edge detection algorithm based on Laplacian of Gaussian (LoG) operator, which has significant acceleration due to the quantum parallelism. Firstly, the quantum circuit of the novel enhanced quantum image representation (NEQR) model is improved to represent multiple quantum images simultaneously with lower time complexity. Secondly, the quantum subtractor and quantum comparator are optimized. Thirdly, quantum adder, quantum multiplier, quantum comparator, and quantum subtractor are adopted to design the quantum circuit of LoG operator and zero-crossing detector, which constitute the quantum edge detection algorithm. Finally, the algorithm is simulated on the IBM Quantum Experience (IBM Q) platform to verify its feasibility. Compared with the existing state-of-the-art algorithms, the proposed quantum image edge detection algorithm has significant improvement in time complexity.
In this work, we propose a novel quantum algorithm for edge detection in digital grayscale images, based on the sequency-ordered Walsh-Hadamard transform. The proposed method significantly improves upon existing quantum techniques for edge detection by using a quantum algorithm for the sequency-ordered Walsh-Hadamard transform, achieving a circuit depth of O(n) (where n is the number of qubits). This represents a notable enhancement over the Quantum Fourier Transform (QFT), which has a circuit depth of O(n2). Furthermore, our approach for edge detection has a computational cost (both gate complexity and quantum circuit depth) of O(log2(N1N2)) for an image of size N1×N2, offering a considerable improvement over the Quantum Hadamard Edge Detection (QHED) algorithm, which incurs a cost of O(poly(log2(N1N2))). By integrating a quantum high-pass filter with the sequency-ordered Walsh-Hadamard transform, the algorithm effectively extracts edge information from images. Computational examples are provided to demonstrate the efficacy of the proposed algorithm which provides a better performance in comparison to QHED.
For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the experimental results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur.