In this review, we present an overview of the advances made in quantum image processing (QIP) comprising of the image representations, the operations realizable on them, and the likely protocols and algorithms for their applications. In particular, we focus on recent progresses on QIP-based security technologies including quantum watermarking, quantum image encryption, and quantum image steganography. This review is aimed at providing readers with a succinct, yet adequate compendium of the progresses made in the QIP sub-area. Hopefully, this effort will stimulate further interest aimed at the pursuit of more advanced algorithms and experimental validations for available technologies and extensions to other domains.
In this review, we present an overview of the advances made in quantum image processing (QIP) comprising of the image representations, the operations realizable on them, and the likely protocols and algorithms for their applications. In particular, we focus on recent progresses on QIP-based security technologies including quantum watermarking, quantum image encryption, and quantum image steganography. This review is aimed at providing readers with a succinct, yet adequate compendium of the progresses made in the QIP sub-area. Hopefully, this effort will stimulate further interest aimed at the pursuit of more advanced algorithms and experimental validations for available technologies and extensions to other domains.
Efficient and reliable storage, analysis, and transmission of medical images are imperative for accurate diagnosis, treatment, and management of various diseases. Since quantum computing can revolutionize big data analytics by providing faster solutions and security tactics, numerous studies in this field have focused on the use of quantum and quantum-inspired algorithms to enhance the performance of traditional medical image processing approaches. This review aims to provide readers with a succinct yet adequate compendium of the advances in medical image processing combined with quantum behaviors for disease diagnosis and medical image security. Some open challenges are outlined, identifying the performance limitations of current quantum technology in their applications, while addressing the short-, medium-, and long-term development plans of this field in designing future quantum healthcare systems. We hope that this review will provide full guidance for upcoming researchers interested in this area and will stimulate further appetite of experts already active in this area aimed at the pursuit of more advanced quantum paradigms in medical image processing applications.
Image processing is one of the many computer domains that has seen quantum computing emerge as a revolutionary technology. This is due to the fact that quantum computing holds the potential of exponential speedups and unique techniques. The purpose of this study is to give a thorough analysis of the applications of quantum computing in image processing. It investigates the ways in which quantum algorithms and hardware might be used to handle traditional issues, such as picture compression, enhancement, pattern detection, and image recovery. Within the scope of this study, the theoretical underpinnings of quantum computing are investigated, recent developments in the field are discussed, and a comparison is made between quantum techniques and classical procedures. In addition to this, it outlines the primary obstacles, such as scalability and hardware limits, that are now preventing the broad use of quantum approaches in image processing. In conclusion, the study provides an overview of prospective future research areas, focusing on the possibility of further integrating quantum computing with sophisticated image processing technologies such as deep learning. The purpose of this work is to provide scholars and practitioners who are interested in the interface of quantum computing and image processing with a basic reference that they may use.
Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing.This paper starts with a brief review of the principles which underlie quantum computing, and also deals with some of the basics of qubits and quantum computing.Then this paper starts to deal with some different methods of image storage, representation and retrieval in a quantum system.This paper also describes the advantages of using those methods in quantum systems compare to classical systems. In the next section, a short discussion on some of the important aspects, comparison among them and applications of quantum image processing is presented. A few other hot topics and open problems in quantum image processing are mentioned in this paper. This review article will provide the readership an overview of progress witnessed in the area of Quantum Image processing while also simulating further interest to pursue more advanced research in it.
Quantum computing is increasingly being viewed as a promising direction for the future of image processing. Conventional approaches to image classification, enhancement, compression, and restoration, although highly developed, often struggle with scalability, computational complexity, and data security—especially when processing high-dimensional or sensitive visual information. This survey explores how quantum computing can help address these limitations through quantum image representation models such as FRQI and NEQR, variational quantum circuits, and hybrid quantum–classical architectures. The primary objective of this review is to present a clear and structured overview of the current state of quantum image processing (QIP). It discusses foundational representation models, emerging hybrid learning techniques, available software frameworks and simulators, benchmarking practices, and practical applications in areas such as medical imaging, remote sensing, and biometric security. Particular attention is given to solutions that are feasible on today’s NISQ-era hardware, including hybrid architectures and quantum-secured image processing schemes. The survey concludes by identifying existing challenges and outlining realistic future research directions. Overall, it emphasizes the practical importance of hybrid models, the need for standardized quantum-oriented benchmarks, and the growing potential of quantum-based security mechanisms for safeguarding visual data.
The basics of Quantum Information Processing (QIP) is summarized below, in addition to a comprehensive survey of quantum image-based data hiding techniques. QIP is more efficient and provides higher security than classical information processing. A comparative analysis of the Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR) model is also presented. QIP is used in various potential applications, hence offering an interesting and demanding area for researchers [1]. Some of the important applications include medicine, military, environmental monitoring, image processing, communications, computations, and cryptography. This article focusses on the processing of information that can be represented using quantum mechanics [2]. Furthermore, coherence, entanglement, and superposition of quantum states makes quantum computing more suitable to its classical counterpart for information storage and processing [1], [3].
Quantum steganography plays a critical role in embedding confidential data into carrier messages using quantum computing schemes. The quantum variant of steganography outperforms its classical counterpart from security, embedding efficiency and capacity, imperceptibility, and time-complexity perspectives. Considerable work has been carried out in the literature focusing on quantum steganography. However, a holistic view of available schemes is missing. This paper provides an overview of latest advances in the field of quantum-steganography and image-steganography schemes. Moreover, the paper includes discussion of improvements made in the aforementioned fields, a brief explanation of the methodologies used for each presented algorithm, and a comparative study of existing schemes.