Blind Image Separation based on Meta-heuristic Optimization Methods and Mutual Information

Abstract

There are a number of modern disciplines in digital signal processing (DSP) as so-called blind images. The core of this problem is there two images mixed in one image and require separate these images and recovering original images. There are many methods and strategies used to solve this problem. One of these solutions is unsupervised machine learning mechanisms, as in the Independent Component Analysis (ICA), which uses the statistical properties of the latent images. This method essentially is dependent upon the statistical characteristics of an observation signals and the non-Gaussian limitations between the mixed images conditions. For all applications, the ICA needs to enhancing, therefore many optimization methods used for that purpose. The swarm intelligence methods are one of many techniques utilized to enhance the ICA’s efficiency. For this purpose, in this paper, three swarm optimization methods used are Quantum Particle Swarm Optimization (QPSO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). These methods implemented, on nine gray-scale images with seven nixing cases, separately. The results are been evaluated under three metrics for assessment are Structural Similarity Index Measurement, Peak Signal to Noise Ratio, and Normalized Cross Correlation. The applying of this system gave optimal results under the specified measurements.