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22 October 2024 Quantum image denoising with machine learning: a novel approach to improve quantum image processing quality and reliability
Yifan Zhou, Yew Kee Wong, Yan Shing Liang
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Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132741B (2024) https://doi.org/10.1117/12.3037909
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yifan Zhou, Yew Kee Wong, and Yan Shing Liang "Quantum image denoising with machine learning: a novel approach to improve quantum image processing quality and reliability", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132741B (22 October 2024); https://doi.org/10.1117/12.3037909
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KEYWORDS
Image processing
Quantum noise
Quantum modeling
Quantum communications
Quantum computing
Image quality
Education and training
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