A Quantum Parallel Self Organizing Neural Network (QPSONN) for Pure Color Object Extraction from a Noisy Background

Abstract

In this article, a quantum version of the parallel self organizing neural network (QPSONN) architecture for extraction of pure color objects from a noisy perspective is proposed. The QPSONN architecture operates in a phased manner to process input noisy pure color images. After the segregation of the pure color inputs into pure color components in the initial phase, these components are subsequently forwarded for processing to three component quantum multilayer self organizing neural network (QMLSONN) architectures composed of three processing layers viz., input, hidden and output layers characterized by qubits based neurons. The interconnection weights are represented by single qub it rotation gates. Quantum measurements at the component output layers destroy the quantum states of the processed information facilitating adjustment of network interconnection weights by a quantum back propagation algorithm using linear indices of fuzziness. Finally, a fusion of the stable component outputs are brought about in a sink layer to produce extracted outputs. Results of application of the QPSONN are demonstrated on a synthetic and a real life spanner image with various degrees of Gaussian noise. A comparison with the classical PSONN architecture reveals the extraction and time efficiency of the proposed QPSONN architecture.