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Logic Journal of IGPL

Exploring quantum edge detection for sheep body contours in images

Santiago Merino-Bajo, Lídia Sánchez-González, Vicente Matellán Olivera

Published May 3, 2025• 0 citations
Publisher

Abstract

Abstract Quantum computing is being explored to tackle complex problems, such as image processing in this specific case. The ability of qubits to exist in multiple states at once and take advantage of quantum phenomena such as superposition and entanglement could have significant applications in a variety of fields, including image processing. Understandably, at this early stage in the development of quantum computing, quantum algorithms are not yet as efficient as classical algorithms for certain tasks. However, as quantum technology advances and becomes more accessible, we are likely to see significant improvements in terms of efficiency and problem-solving capabilities. The use of quantum computing to process images and detect specific features, such as the body contour of sheep for weight assessment or disease detection, has the potential to be a valuable tool in the agriculture and livestock industry. In this paper, we present the results of a quantum edge detector by analysing how the resolution affects the image result. Thus, a quantum edge detection model is implemented in the quantum simulator to analyse the edge recovery capability of an image as a function of the number of pixels in order to test the efficiency of quantum computers for image processing. The experiment results illustrate that edge detection times using quantum algorithms exceed 9000 seconds on 128x128 pixel images. Although the results are worse than those obtained using classical image processing models, they are promising since a resolution of 64$\times $64 or 128$\times $128 is precise enough to determine the body contour of the sheep and can be executed in a quantum simulator. With the evolution of quantum computers, we are approaching the possibility of being able to use them for image processing tasks.

DOI
10.1093/jigpal/jzaf030

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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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Abstract Quantum computing is being explored to tackle complex problems, such as image processing in this specific case. The ability of qubits to exist in multiple states at once and take advantage of quantum phenomena such as superposition and entanglement could have significant applications in a variety of fields, including image processing. Understandably, at this early stage in the development of quantum computing, quantum algorithms are not yet as efficient as classical algorithms for certain tasks. However, as quantum technology advances and becomes more accessible, we are likely to see significant improvements in terms of efficiency and problem-solving capabilities. The use of quantum computing to process images and detect specific features, such as the body contour of sheep for weight assessment or disease detection, has the potential to be a valuable tool in the agriculture and livestock industry. In this paper, we present the results of a quantum edge detector by analysing how the resolution affects the image result. Thus, a quantum edge detection model is implemented in the quantum simulator to analyse the edge recovery capability of an image as a function of the number of pixels in order to test the efficiency of quantum computers for image processing. The experiment results illustrate that edge detection times using quantum algorithms exceed 9000 seconds on 128x128 pixel images. Although the results are worse than those obtained using classical image processing models, they are promising since a resolution of 64×\times ×64 or 128×\times ×128 is precise enough to determine the body contour of the sheep and can be executed in a quantum simulator. With the evolution of quantum computers, we are approaching the possibility of being able to use them for image processing tasks.
In this study, both traditional and quantum image processing methods are considered in order to highlight the boundaries of objects. For efficient encoding and processing of pixel intensities, the use of quantum ventilation, in particular the Hadamard transform, is being studied. Quantum algorithms can work with exponential acceleration compared to classical methods; this is especially important for working with large images. The study examines the influence of quantum transformations on the boundary determination process, as well as methods of amplitude permutations to obtain data on edge elements. The superposition and entanglement of qubits have shown to improve the performance and accuracy of the algorithm when processing multiple boundaries simultaneously. It has now been established that quantum computation of intensity differences between neighboring pixels plays an important role in boundary detection. Experiments with 64x64 pixel images have shown that the quantum algorithm performs well in image processing tasks. It has been substantiated that the use of the quantum approach increases processing speed and reduces computational costs. A quantum circuit of the algorithm with its main transformation stages is presented. In general, pathways for further optimization of the algorithm have been developed, which include reducing the number of qubits and adapting it for more complex images.
Quantum computing is a rapidly developing field with faster computational capabilities than classical computing.The popularity of quantum computing has reached the field of image processing, particularly with a breakthrough method known as Quantum Hadamard Edge Detection.This approach represents a significant advancement in edge detection techniques using quantum computing.Quantum Hadamard Edge Detection is a method that can detect image edges more quickly than classical methods with exponential acceleration.This paper explains the Quantum Hadamard Edge Detection method in detail, including how it is implemented, a time complexity explanation, some experiments, and future research directions.Our experiments utilize a quantum computer simulator and employ four measurement metrics: Structural Similarity Index, Figure of Merit, Entropy, and a Proposed Metric with radius-based features, to detect simple binary images, MNIST images, and the Berkeley Segmentation datasets.We recognize the potential of quantum computing and believe that image processing with quantum representation will make processing more efficient and significantly valuable in the future.
Cancer is a hazardous ailment, becoming even more ominous due to its capacity to remain unnoticed. The significance of cancer research lies in its role in enhancing both the detection and treatment of this disease, ultimately leading to improved quality of life for affected individuals. The process of edge detection assumes a pivotal role in image processing, as it aids in the identification of object boundaries within images. In recent years, quantum computing has garnered substantial attention for its potential to address complex problems more efficiently than classical computers. In this work, we employed edge-detection technique to annotate images related to breast cancer. We have attempted the portion of cancerous nuclei in one biopsy data sample with several malignant nuclei and validated these findings through manual examination. We have used Quantum Hadamard Edge Detection Algorithm (QHED) to identify the nuclei edges. We implemented the algorithm on quantum simulator platforms ie qiskit and Amazon Web Services (AWS). We present the results from implementation on 4 qubits in AWS simulators i.e. State Vector (SV) Simulator and Density Matrix (DM) Simulator. In qiskit implementation, the algorithm was implemented on 4096 x 4096 pixels of biopsy image, that required 24 qubits in the qiskit platform when performing on a CPU. Further, the research presented herein compares the outcomes of Canny edge detection and the results achieved through quantum edge detection from real data and we observe that the results from the quantum approach are at par with the results obtained from the classical approach.
Quantum image processing is a research field that explores the use of quantum computing and algorithms for image processing tasks such as image encoding and edge detection. Although classical edge detection algorithms perform reasonably well and are quite efficient, they become outright slower when it comes to large datasets with high-resolution images. Quantum computing promises to deliver a significant performance boost and breakthroughs in various sectors. Quantum Hadamard Edge Detection (QHED) algorithm, for example, works at constant time complexity, and thus detects edges much faster than any classical algorithm. However, the original QHED algorithm is designed for Quantum Probability Image Encoding (QPIE) and mainly works for binary images. This paper presents a novel protocol by combining the Flexible Representation of Quantum Images (FRQI) encoding and a modified QHED algorithm. An improved edge outline method has been proposed in this work resulting in a better object outline output and more accurate edge detection than the traditional QHED algorithm.
Abstract Edges are image locations where the gray value intensity changes suddenly. They are among the most important features to understand and segment an image. Edge detection is a standard task in digital image processing, solved, for example, using filtering techniques. However, the amount of data to be processed grows rapidly and pushes even supercomputers to their limits. Quantum computing promises exponentially lower memory usage in terms of the number of qubits compared to the number of classical bits. In this paper, we propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era. We compare six variants of the method to reduce the number of circuits and thus the time required for the quantum edge detection. Taking advantage of the scalability of our method, we can practically detect edges in images considerably larger than reached before.
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.