Quantum inspired meta-heuristic algorithms for multi-level thresholding for true colour images

Abstract

In this article, the particle swarm optimization and differential evolution algorithms inspired by the intrinsic principles of quantum mechanics are presented. These quantum versions of meta-heuristic algorithms, namely quantum inspired particle swarm optimization and quantum inspired differential evolution for multi-level thresholding have been designed to find optimal thresholds of colour images at different levels by exploiting Kapur's entropy as an objective function. The average fitness and the standard deviation of the fitness values are reported. The test results over two test images at different levels certify the efficacy of the proposed methods with reference to precision, computational time, and durability over their classical counterparts. At last, a statistical measure, t-test has been performed among the four methods (two quantum methods and two classical methods) taking two methods in a single grasp to ascertain the supremacy of the results.