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A New Framework for Quantum Image Processing and Application of Binary Template Matching

Hasan Yetış, Mehmet Karaköse

Published Feb 16, 2022• 4 citations
Publisher

Abstract

Quantum computing is promising for image processing applications with its parallel processing capability. Today, studies are carried out to perform various image processing operations via quantum computing. In this study, a framework for window-based image processing is proposed. After encoding input images, the proposed framework keeps all the values in the relevant window in separate registers, depending on the window size. Window-based operations can be performed in parallel by applying Hadamard gate to the inputs and performing the related operations on the values in the window. The proposed framework is applied for image matching applications, which is an important branch of image processing. By comparing the searched pattern with the values in the window, it is checked whether it matches the searched pattern. Binary values are used to make the application more understandable.

DOI
10.1109/it54280.2022.9743534

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Quantum computing is promising for image processing applications with its parallel processing capability. Today, studies are carried out to perform various image processing operations via quantum computing. In this study, a framework for window-based image processing is proposed. After encoding input images, the proposed framework keeps all the values in the relevant window in separate registers, depending on the window size. Window-based operations can be performed in parallel by applying Hadamard gate to the inputs and performing the related operations on the values in the window. The proposed framework is applied for image matching applications, which is an important branch of image processing. By comparing the searched pattern with the values in the window, it is checked whether it matches the searched pattern. Binary values are used to make the application more understandable.
Quantum computing stands at a transformative threshold, with error-correcting quantum processors expected to emerge within the next five years. This technological advancement will be crucial for maintaining competitive advantage in fields such as cryptography, optimization, molecular medicine, and image processing, with mainstream quantum computing projected by 2030. This study introduces a novel Quantum Parallel Framework (QPF) designed to prepare for the integration of quantum computing in industrial applications. The research focuses on developing and implementing parallel quantum algorithms for practical commercial applications, particularly in image processing. The QPF provides a comprehensive methodology for organizations to build expertise in quantum computing integration with classical systems. It includes a Qiskit interface for managing quantum algorithms and communications with Quantum Processing Units (QPUs), along with a C++ and OpenGL-based front end. The framework's capabilities are demonstrated through a case study implementing a Quantum Hadamard Edge Detector (QHED) in parallel execution. The study evaluates various performance parameters, including QHED threads, processors, Non-Uniform Memory Allocation (NUMA) strategy, thread priority, and image resolution, to optimize processing efficiency. The framework provides a practical pathway for organizations to develop and mature their quantum computing capabilities, focusing on real-world applications and system integration. This work contributes to the growing field of quantum computing by offering a methodology that can be adapted for various quantum algorithms and applications, particularly in preparation for more powerful self-error correcting QPUs.
A method to compare multiple pairs of quantum images in parallel is proposed, where the similarities of the images are estimated according to the probability distributions of the readouts from quantum measurements. The proposed method by means of a single Hadamard gate with control-conditions to transform the entire information encoding the quantum images in a strip, offers a significant speed-up in comparison to performing the same task on traditional computing devices. Three simulation experiments comprising of the comparison of two images, multiple pairs of images, and the sub-blocks from two images are implemented using MATLAB to demonstrate the feasibility and efficiency of the parallel comparison. The proposal advances a fundamental step towards image searching on quantum computers in which the image with the highest similarity to a particular reference image is retrieved as a search result from a database.
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.
Quantum computation is a promising and rapidly growing field that has witnessed a series of significant breakthroughs in the last several decades.While some practitioners envision the realization of complete quantum computing devices, the research presented in the thesis focusses on the works that we can do with quantum computing hardware when they are realized.One of such areas is that of quantum image processing.A Flexible Representation of Quantum Images (FRQI) allows us to dedicate n-qubits and 1-qubit to capturing the information about the position and color of every pixel in the image, respectively.A method to compare multiple pairs of quantum images in parallel is proposed, where the similarities of the images are estimated according to the probability distributions of the readouts from quantum measurements.It offers a significant speed-up in comparison to performing the same task on traditional computing devices by means of a single Hadamard gate with control-conditions to transform the entire information encoding the quantum images in a strip.The simulation experiments comprising of the comparison of two images, multiple pairs of images, and the sub-blocks from two images are implemented to demonstrate the feasibility and efficiency of the parallel comparison.On the basis of such a comparison strategy, a quantum image searching method is presented where the image in a 4 × 4 binary image database with the highest similarity value to the particular test image is retrieved as the search result using low computational resources.To process the RGB-like quantum image, the representation for Multi-Channel Quantum Images (MCQI) is given, and a double-key, double-domain watermarking strategy for i
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.
High-resolution images are being generated due to the rapid development of image sensors. Various image processing algorithms require images of reduced sizes, given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for many applications. This letter proposes a novel approach to compress images using quantum computing. A comparative study on different standard data encoding techniques used in quantum computing is undertaken. We propose four quantum compression techniques by extending the unitary operations of amplitude embedding for compressing images. The proposed methods provide exponential scaling as amplitude embedding is used, where2n2^{n}2nclassical data values are encoded intonnnqubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms.