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Mechanics and Mechanical Engineering

Restoration from noise pollution for quantum images

Kai Liu, Yi Zhang, Xiao-Ping Wang, Kai Lu

Published Jul 14, 2016• 1 citation
Publisher
DOI
10.1142/9789813145603_0130

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Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.
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.