Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.
Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.
Creating and using quantum algorithms that might perform better than those of conventional computers is the aim of the emerging field of quantum research. QIP has attracted a lot of interest in the last decade. However, the lack of the quantum feature extraction method limits the interpretation of quantum images. FE is the process of extracting relevant details from raw data. Feature extraction is a crucial component of DIP since it significantly affects the accuracy and efficacy of image identification and classification tasks. Features are essential in the field of image processing. It makes it possible for the content of the photographs to be represented as accurately as possible. Before features are obtained, the image is subjected to a variety of image processing procedures, including scaling, filtering, and grey conversion. The features that will be useful for image recognition and classification are then obtained using feature extraction techniques. The purpose of this study was to demonstrate how well the feature extraction technique can be applied to attain high algorithm performance, which in turn improves performance metrics: mean, skewness, contrast, standard deviation, and root mean square error with 125, 4.60, 0.35, 33, and 0.55. This article explains raw data image preprocessing and the quantum feature technique. The results show how effective and superior the proposed method is. We wrap up by providing an overview of this work, pointing out some important discoveries, and outlining potential directions for further research.
The tremendous parallel processing capability of quantum computers allow quantum image processing, a multidisciplinary field combining image processing and quantum computing, to expand the potential outcomes for image processing. The problem of quantum computation is to create effective quantum algorithms since quantum computers need extremely effective algorithms than classical algorithm. Additionally, information storage, communication, and computing power are increasing in relevance with the number and importance of processing digital images. Some of these issues might be resolved by encoding the visual data in quantum-mechanical systems as opposed to classical methods and by switching from classical to quantum information processing. Moreover, the quantum image processing is analysed based on the recent research works and is summarized. This study projects the application of quantum image processing and challenges.
The integration of quantum computing into image processing represents a significant advancement in computational technology, offering transformative capabilities that enhance performance, efficiency, and accuracy. This chapter explores hybrid quantum-classical algorithms, focusing on how quantum subroutines can be integrated into classical image processing workflows. A comparative analysis of various quantum image encoding techniques highlights their strengths and limitations, paving the way for optimized applications across diverse fields. Key advantages of quantum-enhanced image processing, such as improved speed in high-resolution data analysis and superior optimization methods, are discussed in relation to real-world applications, including medical imaging and computer vision. The chapter anticipates future developments in quantum technology, emphasizing the need for interdisciplinary collaboration to unlock the full potential of quantum computing in digital image applications. By addressing the critical gaps in current methodologies, this work aims to inspire further research and innovation in the realm of quantum-enhanced image processing.
If you read that quantum machine learning applications solve some traditional machine learning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded vectors, limit the use of applications and require additional specifications on how to extract practical and useful results. In satellite images, degradation of image contrast and color quality is a common problem, which brings great difficulties for information extraction and visibility of image objects. This is due to atmospheric conditions, which can cause a loss of color and contrast in the image. Regardless of the band, image enhancement plays a vital role in presenting details more clearly. The research includes new computational algorithms that accelerate multispectral satellite imagery to improve land cover mapping and study feature extraction. The first method is a quantum Fourier transform algorithm based on quantum computing, and the second method is a parallel modified data algorithm based on parallel computing. These calculation algorithms are applied to multispectral images for improvement. The quantum-processed image is compared with the original image, and better results are obtained in terms of visual interpretation of the extracted information. Quantitatively evaluate the performance of the improved method and evaluate the applicability of the improved technology to different land types.
This paper presents an evaluation of various quantum algorithms for their potential application in real-time vision applications. By analyzing the underlying principles and mechanisms of key quantum algorithms, including those based on the Quantum Fourier Transform (QFT), amplitude amplification, quantum walks, and quantum optimization, the study aims to assess their suitability for enhancing vision tasks. The computational advantages offered by these quantum algorithms, such as speed and efficiency, are highlighted, particularly in comparison to classical approaches. Algorithms based on amplitude amplification, like Grover’s Algorithm and Quantum Counting, demonstrate significant promise for improving the accuracy and speed of object detection, especially in scenarios requiring real-time processing. Similarly, quantum walk algorithms show potential in navigating structured data efficiently, making them relevant for rapid detection tasks. The paper further explores the integration of quantum algorithms with classical techniques, emphasizing the development of hybrid models, such as Quantum Convolutional Neural Networks (QCNNs). Experimental comparisons between traditional Convolutional Neural Networks (CNNs) and QCNNs reveal that quantum-enhanced models may offer advantages in initial training phases, suggesting enhanced capabilities in feature extraction and pattern recognition. The results indicate that while quantum algorithms hold considerable promise for advancing the field of object detection, particularly in real-time applications, further research is needed to fully realize their potential. This study contributes to the growing field of quantum computing by providing a detailed analysis of how various quantum algorithms can be applied to complex data processing tasks in computer vision, offering new directions for the development of more efficient detection systems.
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.