Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.
Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.
Quantum Image Representation (QIR) is a key element in Quantum Image Processing (QIP) and is essential for the effective management of quantum information. To process quantum images, it is necessary to convert classical digital image data into formats suitable for quantum computers. This conversion involves managing, manipulating, and storing the data efficiently in quantum states. The importance of QIR has spurred numerous studies based on the principles of quantum mechanics, aiming to develop effective methods for representing quantum images. These methods are vital for advancing quantum computing applications related to image processing. In this paper, we will discuss and compare two prominent techniques for encoding images into quantum states: the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR). These methods showcase different approaches to quantum image handing, highlighting the evolving nature of quantum image processing technology.
The arena of image processing is witnessing innovative and technologically advanced approaches which have significantly improved the process by multiple parameters. Due to its application in multiple sectors such as medicine, surveillance, satellite communication, automated vehicles etc., image processing has become one of the most sought-after technologies in computers. The motivation of this paper is to offer an in-depth contrast as well as analysis between Digital Image Processing and Quantum Image Processing concepts. This paper describes the elementary concepts of Digital Image Processing (DIP), core components of DIP including image filtering, padding, thresholding and connected components and fundamental steps of DIP. The process of sampling and quantization as well as the relationship between pixels is explained. A major area of focus in this paper is QIP (Quantum-Image-Processing) as well as the principles of quantum computing such as superposition, entanglement, and interference. Comparison between classical bits and qubits is also described. The paper elaborates Genetic Algorithms used for DIP and QIP algorithms such as FRQI(Flexible-Representation-of-Quantum-Images) and NEQR(Novel-Enhanced-Quantum-Representation). The paper concludes with key observations made during the study of DIP and QIP techniques and a fundamental understanding of suitability of the above-mentioned techniques depending on the problem statement.
In this paper, three techniques of internal image-representation in a quantum computer are compared: Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation of digital images (NEQR), and Quantum Boolean Image Processing (QBIP). All conspicuous technical items are considered in this comparison for complete analysis: i) performance as Classical-to-Quantum (Cl2Qu) interface, ii) characteristics of the employed qubits, iii) sparsity of the used internal registers, iv) number and size of the required registers, v) quality in the outcomes recovering, vi) number of required gates and its consequent accumulated noise, vi) decoherence, and vii) fidelity. These analyses and demonstrations are automatically extended to all variants of FRQI and NEQR. This study demonstrated the practical infeasibility in the implementation of FRQI and NEQR on a physical quantum computer (QPU), while QBIP has proven to be extremely successful on a) the four main quantum simulators on the cloud, b) two QPUs, and c) optical circuits from three labs. Moreover, QBIP also demonstrated its economy regarding the required resources needed for its proper function and its great robustness (immunity to noise), among other advantages, in fact, without any exceptions.
Modern approaches to quantum image encoding are covered in this article, including LPIQE (Low-precision Quantum Image Encoding), FRQI (Functional Representation of Quantum Images), and NEQR (New Improved Quantum Representation). Because it enables effective picture processing on quantum computers with constrained resources, quantum image encoding is a crucial component of quantum computing. The page goes into great length on each method's working principles, benefits, and limitations, as well as the domains in which they can be applied. The benefits of representing binary and color images with quantum qubits are examined, along with the significance of encoding correctness and the effectiveness of employing quantum resources. Finally, the potential of quantum picture encoding for the advancement of data compression, image processing, and novel computing technologies is explored. Examples of algorithms, explanations of their properties, and potential uses in image processing and quantum computing are all covered in the article.
Abstract Quantum Image Representation (QIR) is a fundamental concept in quantum image processing. It describes how classical images are converted into quantum states for processing on quantum computers. Researchers proposed several QIR models, with FRQI and NEQR being the most popular ones. These models show the possibilities of the quantum image encoding. Nevertheless, they are also limited in some way. Mostly, pixel intensity accuracy, the required number of qubits, and circuit complexity have trade-offs. Their application to Noisy Intermediate-Scale Quantum (NISQ) devices becomes challenging, the quantum resources being small in such devices, due to this. In the following paper, we introduce our own approach, which is the Intensity-Preserving Quantum image Representation (IP-QIR). The key aim of IP-QIR is to maintain the information about the grayscale intensities and decrease the amount of quantum resources. IP-QIR quantifies pixel intensities by a controlled rotation scheme. The measurement statistics of a single qubit hold the intensity information and the position qubits represent the spatial information. The strategy uses small patches of images rather than complete images to make it easy to implement. This will decrease the depth of circuit design and makes the approach less advanced to the near-term quantum hardware. IBM Qiskit simulations are used in assessing the performance of IP-QIR. The three types of grayscale images of which experiments are run are the synthetic image patches, SAR images and medical TB chest X-ray images. It is revealed that IP-QIR maintains the intensity information compared to the FRQI and NEQR. SAR and medical data can reach values of fidelity of 84.12\%. The other notable point is that the size of a 4 x 4 image patch only needs five qubits which are much lower than in NEQR. Meanwhile, good reconstruction accuracy is achieved. These findings demonstrate the fact that IP-QIR is an effective and realistic approach to quantum image representation on the NISQ-era quantum devices.
This paper examines Quantum Image Representation Algorithms (QIRA), which have significantly advanced the encoding and manipulation of visual data using quantum computing. It explores key developments in image representation techniques, focusing on Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation (NEQR), Generalized Quantum Image Representation (GQIR), Bit Plane Representation of Quantum Images (BRQI), and Order Encoding Quantum Image Model (OQIM). Using the Qiskit framework, these methods are implemented to encode classical images into quantum states and perform various quantum operations. The paper provides a comprehensive understanding of QIRA by outlining the background of these representation techniques and rigorously evaluating their performance in image reconstruction. The evaluation is based on metrics such as Mean Squared Error (MSE), Cosine Similarity, Normalized Cross-Correlation (NCC), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR).