Histogram Driven Fusion of Set of Images Using Multi-thresholding and Optimization for WSN

The recent developments of the Wireless Sensor Network (WSN) with low power cameras have made the possibility of transferring the raw images to the remote areas at any time. As a result, the energy consumption during computation is more. To alleviate this problem, a mechanism called image fusion using histogram-based multi-thresholding with entropy and optimization technique is proposed in this paper. Here, the impact of the blurred effect over the images is also reduced to some extent by averaging the pixels using the entropy. But the entropy affects the computational complexity of the proposed fusion algorithm during the identification of the optimal thresholds. Hence, the optimization technique named improved harmony search algorithm is used to maximize the entropy with less computational time. Finally, by comparing the threshold values of each input images, the set of optimal thresholds create the approximate histogram of the fused output. In this paper, the selection of the number of thresholds decides the computational complexity and the quality of the proposed fusion algorithm. From the simulation results, without compromising the performance metrics and the quality of the image, the number of bits required for representing the proposed fused output is very less compared to that of the other standard image fusion methods. KeywordEntropy, Multi-thresholding, Optimization, Fusion, Harmony Search Algorithm