Quantum image processing (QIP) is still a promising area as the noise of qubit, decoherence and limitations of encoding make it difficult for the current quantum hardware.This paper presents a combined Intelligent Quantum Image Denoising Framework, which uses FRQI and deep learning-based restoration, to enhance the quality and trustworthiness of images in quantum-classical hybrid areas.The short overview of related studies points out the classical filters and shallow neural models denoising methods, which are not able to generalize to quantum noise.To fill this void, a Convolutional Neural Network (CNN) is trained on the FRQI-encoded images simulated with Gaussian, saltand-pepper, and depolarizing noise.The approach uses confidence-based thresholding for restoration of adaptive pixels and measures the results with PSNR, SSIM, and MSE metrics.The experimental outcomes exhibit a considerable rise in the signal-to-noise ratio and the integrity of the structure when compared to the standard denoising techniques.The research indicates that the suggested combination of different methods has the capability to get rid of quantum noise yet at the same time allow the main image features to be seen.The steps for the next research will be applying the model to actual quantum simulators like Qiskit, creating better qubit usage, and widening the application of the framework to high-resolution images for quantum vision in real-world scenarios.
Quantum image processing (QIP) is still a promising area as the noise of qubit, decoherence and limitations of encoding make it difficult for the current quantum hardware.This paper presents a combined Intelligent Quantum Image Denoising Framework, which uses FRQI and deep learning-based restoration, to enhance the quality and trustworthiness of images in quantum-classical hybrid areas.The short overview of related studies points out the classical filters and shallow neural models denoising methods, which are not able to generalize to quantum noise.To fill this void, a Convolutional Neural Network (CNN) is trained on the FRQI-encoded images simulated with Gaussian, saltand-pepper, and depolarizing noise.The approach uses confidence-based thresholding for restoration of adaptive pixels and measures the results with PSNR, SSIM, and MSE metrics.The experimental outcomes exhibit a considerable rise in the signal-to-noise ratio and the integrity of the structure when compared to the standard denoising techniques.The research indicates that the suggested combination of different methods has the capability to get rid of quantum noise yet at the same time allow the main image features to be seen.The steps for the next research will be applying the model to actual quantum simulators like Qiskit, creating better qubit usage, and widening the application of the framework to high-resolution images for quantum vision in real-world scenarios.
Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images. However, QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. In this research we propose a novel approach to address the issue of noise in QIP. By training and employing a machine learning model that identifies and corrects the noise in quantum processed images, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency. The model is trained by learning a dataset consisting of both existing processed images and quantum processed images from open access datasets. This model will be capable of providing us with the confidence level for each pixel and its potential original value. To assess the model's accuracy in compensating for loss and decoherence in QIP, we evaluate it using three metrics: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS). Additionally, we discuss the applicability of our model across domains well as its cost effectiveness compared to alternative methods.
Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images. However, QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. In this research, we propose a novel approach to address the issue of noise in QIP. By training and employing a machine learning model that identifies and corrects the noise in quantum-processed images, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency. The model is trained by learning a dataset consisting of both existing processed images and quantum-processed images from open-access datasets. This model will be capable of providing us with the confidence level for each pixel and its potential original value. To assess the model's accuracy in compensating for loss and decoherence in QIP, we evaluate it using three metrics: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS). Additionally, we discuss the applicability of our model across domains well as its cost effectiveness compared to alternative methods.
Quantum imaging systems produce images with distinctive noise patterns that conventional denoising algorithms cannot effectively process. We present an innovative neural network architecture that merges quantum physics principles with deep learning to address this challenge. Our hybrid approach adapts standard image processing techniques to handle quantum-specific noise while preserving critical image features. Experimental validation demonstrates a consistent 12.6% improvement in output quality compared to existing methods, with efficient performance on standard computing hardware. Additionally, the model exhibits strong generalization capabilities, achieving robust performance across varying noise levels. This advancement represents an important step toward practical quantum imaging applications in fields ranging from medical diagnostics to secure communications.
Medical image denoising remains challenging because imaging modalities and acquisition conditions produce heterogeneous and non-stationary noise. Although recent deep-learning and diffusion-based approaches achieve strong restoration performance, many methods rely on fixed transformations or require substantial computational resources. This work proposes the Noise-Adaptive Quantum Attention Network (NAQAN), a hybrid quantum–classical framework for noise-conditioned medical image denoising. NAQAN combines a lightweight convolutional encoder–decoder with an eight-qubit variational quantum attention mechanism whose circuit parameters are dynamically modulated by a specified noise-level conditioning variable. Feature-wise Linear Modulation (FiLM) provides complementary classical conditioning, while residual learning is used for image reconstruction. The present implementation is noise-conditioned rather than fully blind; the corresponding noise level is supplied to the model during both training and evaluation. Experiments on RetinaMNIST, OCTMNIST, Diabetic Retinopathy, and BraTS MRI under Gaussian, speckle, and Poisson corruption demonstrate consistent restoration performance across different imaging modalities and noise conditions. On BraTS MRI, NAQAN achieves a PSNR of 39.11dB and an SSIM of 0.9548. Controlled comparisons with classical denoising baselines and a parameter-matched classical attention variant further evaluate the contribution of the proposed quantum attention mechanism. NAQAN performs denoising through a single forward inference pass; however, simulation of the quantum circuit introduces additional computational overhead relative to the matched classical variant. These results demonstrate the potential of noise-conditioned hybrid quantum–classical attention for medical image restoration while motivating further evaluation on physical quantum hardware and clinically oriented downstream tasks.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.
Quantum Image Representation (QIR) has become a key component of Quantum Image Processing (QIP), enabling the encoding and manipulation of visual data within quantum computing environments.Its integration with Quantum Artificial Intelligence (QAI) and Quantum Machine Learning (QML) has attracted significant research interest due to the potential advantages of quantum parallelism and compact data representation.This survey reviews major QIR techniques, analyzes their encoding strategies and computational characteristics, and examines their opportunities, challenges, and future relevance in quantum AI and machine learning applications.A systematic review of 70 peer-reviewed journal and conference publications published between 1992 and 2025 was conducted.The selected studies were evaluated based on encoding mechanisms, qubit requirements, circuit complexity, retrieval capability, scalability, and suitability for quantum image processing and learning tasks.The review covers prominent QIR models, including FRQI, NEQR, MCQI, QPIE, GQIR, QRMW, and tensor-network-based representations.Comparative analysis reveals important trade-offs between qubit efficiency, encoding complexity, retrieval accuracy, and scalability.Amplitude-based approaches provide compact image encoding but suffer from probabilistic retrieval, whereas basis-state representations offer deterministic recovery at the expense of increased circuit complexity.QIR demonstrates significant potential in image classification, feature extraction, image compression, and hybrid quantum-classical learning frameworks.QIR provides a promising foundation for next-generation quantum-enhanced visual intelligence systems.However, practical implementation remains constrained by state preparation overhead, noise, decoherence, limited qubit availability, and the lack of standardized evaluation frameworks.Future research should focus on scalable encoding methods, fault-tolerant quantum architectures, benchmarking standards, and deeper integration with advanced QML models to accelerate the development of practical quantum image analytics.