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Sumer Journal for Pure Science

(QPIE:QHED) Edge Detection Using Optimal Feature Subset Selection Based on Detection of for Quantum Image Processing

زينب حمودي نوري ابو طبرة, Zainab Hammoodi Noori

Published Jun 26, 2026• 0 citations
OpenAlex

Abstract

Image processing and detection are natural extensions of detecting the edges, curves, and areas of sudden changes in brightness or, more specifically, missing pixels. Edge detection is a set of mathematical algorithms. Change detection is a term used to describe the process of identifying discrete changes in single-digit data. The dimension. The ability to detect and remove image edges is fundamental to image processing and computer and machine vision. One way to detect and remove features from images is to use edge detection. In this work, we first provide an overview of research in this area, and then list some of the concerns that have been raised regarding rapid development and its achievement. We see “quantum image classification and recognition” as the ideal application to showcase the potential of quantum technology. Quantum Probabilistic Image Encoding (QPIE:QHED) was used in our study to transform classical data into quantum states. It is necessary to apply quantum algorithms to classical problems, a method for quantum Hadamard edge detection, and also uses them to process quantum image data, often produced by QPIE, using QHED, a modern edge detection technique. Quite simply, QHED is a technology for precise edge detection in images that uses quantum computing.

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Image processing and detection are natural extensions of detecting the edges, curves, and areas of sudden changes in brightness or, more specifically, missing pixels. Edge detection is a set of mathematical algorithms. Change detection is a term used to describe the process of identifying discrete changes in single-digit data. The dimension. The ability to detect and remove image edges is fundamental to image processing and computer and machine vision. One way to detect and remove features from images is to use edge detection. In this work, we first provide an overview of research in this area, and then list some of the concerns that have been raised regarding rapid development and its achievement. We see “quantum image classification and recognition” as the ideal application to showcase the potential of quantum technology. Quantum Probabilistic Image Encoding (QPIE:QHED) was used in our study to transform classical data into quantum states. It is necessary to apply quantum algorithms to classical problems, a method for quantum Hadamard edge detection, and also uses them to process quantum image data, often produced by QPIE, using QHED, a modern edge detection technique. Quite simply, QHED is a technology for precise edge detection in images that uses quantum computing.
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.
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.
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.
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)\mathcal{O}(n)O(n) (where nnn is the number of qubits). This represents a notable enhancement over the Quantum Fourier Transform (QFT), which has a circuit depth of O(n2)\mathcal{O}(n^{2})O(n2). Furthermore, our approach for edge detection has a computational cost (both gate complexity and quantum circuit depth) of O(log⁡2(N1N2))\mathcal{O}(\log_{2}(N_{1}N_{2}))O(log2​(N1​N2​)) for an image of size N1×N2N_{1}\times N_{2}N1​×N2​, offering a considerable improvement over the Quantum Hadamard Edge Detection (QHED) algorithm, which incurs a cost of O(poly(log⁡2(N1N2)))\mathcal{O}(\text{poly}(\log_{2}(N_{1}N_{2})))O(poly(log2​(N1​N2​))). 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.
Quantum image processing and its various applications developed in the last few years. These applications utilize different quantum algorithms for higher accuracy and reduced time complexity compared to classical computing. This paper reviews some applications of quantum image processing like edge extraction, Security, and denoising of images. Applications like feature extraction, machine vision, and feature detection use edge extraction. In this paper, we observe the field of Security by dividing it into two sub-areas, i.e., cryptography and information hiding, used for authentication purposes. For removal of noise signals from input images, the method of denoising is used.
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 64or12864 or 12864or128\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.