A quantum parallel bi-directional self-organizing neural network (QPBDSONN) architecture for extraction of pure color objects from noisy background

Abstract

This paper is aimed to propose a suitable real-time pure color image denoising procedure using a self-supervised network referred to as Quantum Parallel Bi-directional Self-Organizing Neural Network (QPBDSONN) architecture. The proposed QPBDSONN replicates the Parallel Bi-directional Self-Organizing Neural Network (PBDSONN) architecture and exploits by the power of quantum computation. To process three distinct basic color components (Red, Green and Blue) of noisy color image, QPBDSONN uses trinity of Quantum Bi-directional Self-organizing Neural Network (QBDSONN) architecture at the source layer in parallel mode. Each constituent QBDSONN comprises input, intermediate or hidden and output layers interconnected by 8-connected neighborhood topology based layer of neurons represented by qubits. Each constituent QBDSONN updates weighted interconnections in the form of quantum states through counter-propagation between hidden and output layer to obviate quantum back propagation. Rotation gates are introduced to represent weighted inter-links and values of activation. Finally, a quantum measurement operation is performed at the output layer of each constituent QBDSONN followed by fusion operation at the sink layer of QPBDSONN to concatenate the processed color image components resulting in the true output. The superiority of the proposed network architecture over the classical PBDSONN can be established using a real-life spanner pure color image and synthetic pure color image corrupted with various intensity of uniform and Gaussian noise in terms of extraction time and shapes.