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.
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.
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 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.
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.
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.
Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. Quantum image processing offers a promising alternative, but current implementations face significant challenges, such as time-consuming data acquisition, complex device requirements, and limited real-time processing capabilities. This work presents a novel paired transform-based quantum representation for efficient image processing. This representation enables the parallelization of convolution operations, simplifies gradient calculations, and facilitates the processing of one-dimensional and two-dimensional signals. We demonstrate that our approach achieves improved processing speed compared to classical methods while maintaining comparable accuracy. The successful implementation of real-world images highlights the potential of this research for large-scale quantum image processing, architecture-specific optimizations, and applications beyond edge detection.
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.