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

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.