NDLI: A Quantum Bi-Directional Self-Organizing Neural Network (QBDSONN) for binary image denoising

Content Provider	IEEE Xplore Digital Library
Author	Konar, D. Bhattacharyya, S. Das, N. Panigrahi, B.K.
Copyright Year	2015
Description	Author affiliation: Dept. of CSE, Sikkim Manipal Inst. of Technol., Majitar, India (Konar, D.) \|\| Dept. of CSE, Jadavpur Univ., Kolkata, India (Das, N.) \|\| Dept. of IT, RCC Inst. of Inf. Technol., Kolkata, India (Bhattacharyya, S.) \|\| Dept. of EE, IIT Delhi, Delhi, India (Panigrahi, B.K.)
Abstract	A Quantum Bi-directional Self-Organizing Neural Network (QBDSONN) architecture suitable for binary image denoising in real time is proposed in this article. It is composed of three second order neighborhood topology based interconnected layers of neurons (represented by qubits) known as input, intermediate and output layers. Moreover, it does not use any quantum back-propagation algorithm for the adjustment of its interconnection weights. Instead, it resorts to a counter-propagation of quantum states of the intermediate layer and the output layer. In the proposed architecture, the inter-connection weights and activation values are represented by rotation gates. The quantum neurons of each network layer follow a cellular network pattern and are fully intra-connected to each other. QBDSONN self-organizes the quantized input image information by means of the counter-propagating fashion of the quantum network states of the intermediate and output layers of the architecture. A quantum measurement at the output layer collapses superposition of quantum states of the processed information thereby yielding the desired outputs once the network attains stability. Applications of QBDSONN are demonstrated on the denoising of a synthetic and real life spanner image with different degrees of uniform noise and Gaussian noise. Comparative results indicate that QBDSONN outperforms its classical counterpart in terms of time and also it retains the shapes of the denoised images with great precision.
Starting Page	1225
Ending Page	1230
File Size	425677
Page Count	6
File Format	PDF
ISBN	9781479987900
e-ISBN	9781479987924
DOI	10.1109/ICACCI.2015.7275780
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantum Computing Quantum computing Neurons Quantum mechanics Quantum bidirectional self-organizing neural network Computer architecture Bidirectional control Logic gates Bi-directional self-organizing neural network Binary object extraction Biological neural networks
Content Type	Text
Resource Type	Article

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