Quantum Image Processing Tutorial
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Quantum Image Processing in Practice
Author: Artyom M. Grigoryan
language: en
Publisher: John Wiley & Sons
Release Date: 2025-04-01
Comprehensive resource addressing the need for a quantum image processing machine learning model that can outperform classical neural networks Quantum Image Processing in Practice explores the transformative potential of quantum color image processing across various domains, including biomedicine, entertainment, economics, and industry. The rapid growth of image data, especially in facial recognition and autonomous vehicles, demands more efficient processing techniques. Quantum computing promises to accelerate digital image processing (DIP) to meet this demand. This book covers the role of quantum image processing (QIP) in quantum information processing, including mathematical foundations, quantum operations, image processing using quantum filters, quantum image representation, and quantum neural networks. It aims to inspire practical applications and foster innovation in this promising field. Topics include: Qubits and Quantum Logic Gates: Introduces qubits, the fundamental data unit in quantum computing, and their manipulation using quantum logic gates like Pauli matrices, rotations, the CNOT gate, and Hadamard matrices. The concept of entanglement, where qubits become interconnected, is also explored, highlighting its importance for applications like quantum teleportation and cryptography. Two and Multiple Qubit Systems: Demonstrates the importance of using two qubits to process color images, enabling image enhancement, noise reduction, edge detection, and feature extraction. Covers the tensor product, Kronecker sum, SWAP gate, and local and controlled gates. Extends to multi-qubit superpositions, exploring local and control gates for three qubits, such as the Toffoli and Fredkin gates, and describes the measurement of superpositions using projection operators. Transforms and Quantum Image Representations: Covers the Hadamard, Fourier, and Heap transforms and their circuits in quantum computation, highlighting their applications in signal and image processing. Introduces the quantum signal-induced heap transform for image enhancement, classification, compression, and filtration. Explores quantum representations and operations for images using the RGB, XYZ, CMY, HSI, and HSV color models, providing numerous examples. Fourier Transform Qubit Representation: Introduces a new model of quantum image representation, the Fourier transform qubit representation. Describes the algorithm and circuit for calculating the 2-D quantum Fourier transform, enabling advancements in quantum imaging techniques. New Operations and Hypercomplex Algebra: Presents new operations on qubits and quantum representations, including multiplication, division, and inverse operations. Explores hypercomplex algebra, specifically quaternion algebra, for its potential in color image processing. Quantum Neural Networks (QNNs): Discusses QNNs and their circuit implementation as advancements in machine learning driven by quantum mechanics. Summarizes various applications of QNNs and current trends and future developments in this rapidly evolving field. The book also addresses challenges and opportunities in QIP research, aiming to inspire practical applications and innovation. It is a valuable resource for researchers, students, and professionals interested in the intersection of quantum computing and color image processing applications, as well as those in visual communications, multimedia systems, computer vision, entertainment, and biomedical applications.
Quantum Image Processing
This book provides a comprehensive introduction to quantum image processing, which focuses on extending conventional image processing tasks to the quantum computing frameworks. It summarizes the available quantum image representations and their operations, reviews the possible quantum image applications and their implementation, and discusses the open questions and future development trends. It offers a valuable reference resource for graduate students and researchers interested in this emerging interdisciplinary field.
Remote Sensing Image Processing Algorithms for Detecting Air Turbulence Patterns
Injuries due to air turbulence has increased recently, therefore there is considerable concern and interest in understanding and detecting it more accurately. Presently hardly any research deals with air turbulence detection using remote sensing images. Most works use conventional optical remote sensing data with classical methods such as a library spectral signature, band ratio, and principal component analysis without designating new methods and technology. Very little research has attempted to implement optical and microwave remote sensing images for air turbulence detections. This book provides new image processing procedures for air turbulence detection using advanced remote sensing images and quantum image processing. Currently, there is a huge gap between research work in the field of air turbulence detection and advanced remote sensing technology. Most of the theories are not operated in terms of software modules. Most of the software packages in the field of remote sensing images cannot deal with advanced image processing techniques in air turbulence detections due to heavy mathematics work. In this view, this book fills a gap between advanced remote sensing technology and air turbulence detection. For instance, quantum image processing with a new generation of remote sensing technology such as RADARSAT-2 SAR images is also implemented to provide accurate air turbulence detections.