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Journal of Computer Applications

Quantum-inspired image decomposition and edge detection

Wusheng Xu

Published Jan 1, 2013• 1 citation
OpenAlex

Abstract

A new approach based on quantum theory for the decomposition of image was proposed in this paper.An image can be decomposed into a series of characteristic sub-images according to the relation between the pixels built by means of a superposition of states in quantum mechanics in this approach.The characteristics of these sub-images were analyzed and a rule used to generate an operator for the detection of image edge was given on the basis of this.A new algorithm for image edge detection was proposed with the rule.The computer simulation results show the effectiveness of the rule and the superiority of the algorithm proposed here.

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The grey scale of the pixels in image is expressed by quantum bit and the relation between two pixels is described by means of the superposition of two quantum bit in this paper.Based on this,the average of linear hermitian operator in this superposition can be used for judging the edge of image and a new method for edge detection of image is so created.The rationality of the method is interpreted by probability theory here.A function of this new edge detecting method to compensate defect of human’s vision is proved theoretically and is verified in computer simulation.The edge caused by tiny change in grey scale of pixel in high or low grey scale area of image can be detected by using this method.

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Edge detection algorithm based on quantum superposition principle and photons arrival probability

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The detection of object edges in images is a crucial step employed in a vast amount of computer vision applications, for which a series of different algorithms has been developed in the last decades. This paper proposes a new edge detection method based on quantum information, which is achieved in two main steps: (i) an image enhancement stage that employs the quantum superposition law and (ii) an edge detection stage based on the probability of photon arrival to the camera sensor. The proposed method has been tested on synthetic and real images devoted to agriculture applications, where Fram & Deutsh criterion has been adopted to evaluate its performance. The results show that the proposed method gives better results in terms of detection quality and computation time compared to classical edge detection algorithms such as Sobel, Kayyali, Canny and a more recent algorithm based on Shannon entropy.

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Quantum Algorithm for Edge Detection

越 李

边缘检测是图像处理中的基本问题之一,边缘检测的许多算法是基于经典的图像处理技术。本文将介绍基于量子力学框架下的边缘检测量子算法,及其图像特征向量技术在图像检测中的应用。量子算法基于图像经典信息的量子化,通过图像处理中各类元素与量子力学基本物理量一一对应,通过图像特征构造的系统的哈密顿量,应用量子演化技术学习图像的轮廓,从而,给出我们期望的图像轮廓。我们应用像素灰度梯度、像素灰度的二阶差分、Sobel算子卷积等方法作为特征向量来构造哈密顿量。通过一组标准图(从BSDS500公开数据集的图片)测试我们的量子算法,结果显示我们的量子算法的灵敏度比传统Canny算法提高了25%,而且具有更强的抗噪声能力。 Edge detection is one of the fundamental problems in the image processing. Many algorithms of edge detection are based on the classical image processing technology. In this paper, we introduce the quantum algorithm of edge detection based on quantum mechanics and its application in image detection. Quantum algorithm is based on the quantization of the classical information of the image and one-to-one correspondence between various elements of the image processing and the concept and variables of quantum mechanics. By constructing the Hamiltonian for the image features, the quantum evolution technology for the quantum information of images learns the profile of the image, so as to give the desired image contour. We use the techniques of the pixel gray gradient, second order difference of pixel gray, Sobel operator convolution as the feature vectors to construct Hamiltonian. Our quantum algorithms are tested by a set of standard pictures (coming from BSDS500 exposed database). The testing results show that our quantum algorithm has a 25% higher sensitivity than the traditional Canny algorithm and has a stronger denoise ability.

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Quantum Edge Detection for Image Segmentation in Optical Environments

Mario Mastriani

A quantum edge detector for image segmentation in optical environments is presented in this work. A Boolean version of the same detector is presented too. The quantum version of the new edge detector works with computational basis states, exclusively. This way, we can easily avoid the problem of quantum measurement retrieving the result of applying the new detector on the image. Besides, a new criterion and logic based on projections onto vertical axis of Bloch's Sphere exclusively are presented too. This approach will allow us: 1) a simpler development of logic quantum operations, where they will closer to those used in the classical logic operations, 2) building simple and robust classical-to-quantum and quantum-to-classical interfaces. Said so far is extended to quantum algorithms outside image processing too. In a special section on metric and simulations, a new metric based on the comparison between the classical and quantum versions algorithms for edge detection of images is presented. Notable differences between the results of classical and quantum versions of such algorithms (outside and inside of quantum computer, respectively) show the existence of implementation problems involved in the experiment, and that they have not been properly modeled for optical environments. However, although they are different, the quantum results are equally valid. The latter is clearly seen in the computer simulations

arXiv:1409.2918•Sep 9, 2014•9 citations
A new approach based on quantum theory for the decomposition of image was proposed in this paper.An image can be decomposed into a series of characteristic sub-images according to the relation between the pixels built by means of a superposition of states in quantum mechanics in this approach.The characteristics of these sub-images were analyzed and a rule used to generate an operator for the detection of image edge was given on the basis of this.A new algorithm for image edge detection was proposed with the rule.The computer simulation results show the effectiveness of the rule and the superiority of the algorithm proposed here.
The grey scale of the pixels in image is expressed by quantum bit and the relation between two pixels is described by means of the superposition of two quantum bit in this paper.Based on this,the average of linear hermitian operator in this superposition can be used for judging the edge of image and a new method for edge detection of image is so created.The rationality of the method is interpreted by probability theory here.A function of this new edge detecting method to compensate defect of human’s vision is proved theoretically and is verified in computer simulation.The edge caused by tiny change in grey scale of pixel in high or low grey scale area of image can be detected by using this method.
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.
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.
The detection of object edges in images is a crucial step employed in a vast amount of computer vision applications, for which a series of different algorithms has been developed in the last decades. This paper proposes a new edge detection method based on quantum information, which is achieved in two main steps: (i) an image enhancement stage that employs the quantum superposition law and (ii) an edge detection stage based on the probability of photon arrival to the camera sensor. The proposed method has been tested on synthetic and real images devoted to agriculture applications, where Fram & Deutsh criterion has been adopted to evaluate its performance. The results show that the proposed method gives better results in terms of detection quality and computation time compared to classical edge detection algorithms such as Sobel, Kayyali, Canny and a more recent algorithm based on Shannon entropy.
边缘检测是图像处理中的基本问题之一,边缘检测的许多算法是基于经典的图像处理技术。本文将介绍基于量子力学框架下的边缘检测量子算法,及其图像特征向量技术在图像检测中的应用。量子算法基于图像经典信息的量子化,通过图像处理中各类元素与量子力学基本物理量一一对应,通过图像特征构造的系统的哈密顿量,应用量子演化技术学习图像的轮廓,从而,给出我们期望的图像轮廓。我们应用像素灰度梯度、像素灰度的二阶差分、Sobel算子卷积等方法作为特征向量来构造哈密顿量。通过一组标准图(从BSDS500公开数据集的图片)测试我们的量子算法,结果显示我们的量子算法的灵敏度比传统Canny算法提高了25%,而且具有更强的抗噪声能力。 Edge detection is one of the fundamental problems in the image processing. Many algorithms of edge detection are based on the classical image processing technology. In this paper, we introduce the quantum algorithm of edge detection based on quantum mechanics and its application in image detection. Quantum algorithm is based on the quantization of the classical information of the image and one-to-one correspondence between various elements of the image processing and the concept and variables of quantum mechanics. By constructing the Hamiltonian for the image features, the quantum evolution technology for the quantum information of images learns the profile of the image, so as to give the desired image contour. We use the techniques of the pixel gray gradient, second order difference of pixel gray, Sobel operator convolution as the feature vectors to construct Hamiltonian. Our quantum algorithms are tested by a set of standard pictures (coming from BSDS500 exposed database). The testing results show that our quantum algorithm has a 25% higher sensitivity than the traditional Canny algorithm and has a stronger denoise ability.
A quantum edge detector for image segmentation in optical environments is presented in this work. A Boolean version of the same detector is presented too. The quantum version of the new edge detector works with computational basis states, exclusively. This way, we can easily avoid the problem of quantum measurement retrieving the result of applying the new detector on the image. Besides, a new criterion and logic based on projections onto vertical axis of Bloch's Sphere exclusively are presented too. This approach will allow us: 1) a simpler development of logic quantum operations, where they will closer to those used in the classical logic operations, 2) building simple and robust classical-to-quantum and quantum-to-classical interfaces. Said so far is extended to quantum algorithms outside image processing too. In a special section on metric and simulations, a new metric based on the comparison between the classical and quantum versions algorithms for edge detection of images is presented. Notable differences between the results of classical and quantum versions of such algorithms (outside and inside of quantum computer, respectively) show the existence of implementation problems involved in the experiment, and that they have not been properly modeled for optical environments. However, although they are different, the quantum results are equally valid. The latter is clearly seen in the computer simulations