Binary image denoising using a quantum multilayer self organizing neural network |
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Affiliation: | 1. Faculty of Information Technology, Multimedia University, Cyberjaya, Malaysia;2. Faculty of Creative Industries, Universiti Tunku Abdul Rahman, Malaysia;3. Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia;1. Department of Computer Science, Rani Anna Government College for Women, Tirunelveli, Tamil Nadu, India;2. Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India;1. School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Viet Nam;2. Faculty of Information Technology, Le Quy Don Technical University, Hanoi, Viet Nam;3. Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Japan;1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China;2. University of Texas at San Antonio, United States |
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Abstract: | Several classical techniques have evolved over the years for the purpose of denoising binary images. But the main disadvantages of these classical techniques lie in that an a priori information regarding the noise characteristics is required during the extraction process. Among the intelligent techniques in vogue, the multilayer self organizing neural network (MLSONN) architecture is suitable for binary image preprocessing tasks.In this article, we propose a quantum version of the MLSONN architecture. Similar to the MLSONN architecture, the proposed quantum multilayer self organizing neural network (QMLSONN) architecture comprises three processing layers viz., input, hidden and output layers. The different layers contains qubit based neurons. Single qubit rotation gates are designated as the network layer interconnection weights. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing incorporation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a quantum backpropagation algorithm.Results of application of the proposed QMLSONN are demonstrated on a synthetic and a real life binary image with varying degrees of Gaussian and uniform noise. A comparative study with the results obtained with the MLSONN architecture and the supervised Hopfield network reveals that the QMLSONN outperforms the MLSONN and the Hopfield network in terms of the computation time. |
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Keywords: | Image denoising MLSONN Hopfield network Fuzzy set theory Quantum computing Quantum multilayer self organizing neural network |
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