Pure color object extraction from a noisy state using quantum version parallel self organizing neural network

摘要

The classical techniques used for noisy object extraction recline that some a priori information concerning the noise characteristics is required during the extraction process. The proposed quantum version parallel self-organizing neural network (QVPSONN) architecture uses the quantum characteristics like superposition, coherence, decoherence, entanglement, etc. of quantum principle for its operation. The extraction of object achieved is better as well as the extraction time is reduced. QVPSONN architecture uses the phase shifting property of qubits to extract the objects from the noisy environment. At first the pure color input image is separated into three color components into the source layer. Then, the three color components are fed to the three parallel architectures of QMLSONNs for processing which are finally fused in the sink layer. Each of the processing layers of QVPSONN comprises qubit-based neurons. The weights between the network layers are demonstrated by single qubit rotation gates. Quantum measurement is applied for processing the information at the output layers, whereby the quantum states are destroyed using the decoherence property. When the system becomes stable, the sink layer fuses it and generates the output. Results of application on a synthetic image and a real-life reveal its efficacy.