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Optimized ensemble decision-based multi-focus imagefusion using binary genetic Grey-Wolf optimizer in camera sensor networks

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Abstract

Modern developments in image technology enabled easy access to an innovative type of sensor-based networks, Camera or Visual Sensor Networks (VSN). Nevertheless, more sensor data sources bring about the problem of overload information. To solve this problem, some researchers have been carried out on the techniques to counteract the data overload caused by sensors without losing useful data. The aim of fusion in each application is to combine images from several sensors, which leads to the decreased amount of input image data, producing an image with more accurate data. This paper proposes a noisy feature removal scheme for multi-focus image fusion combining the decision information of optimized individual features. The proposed scheme is developed in two main steps. In the first step, the diverse types of features are extracted from each block of input blurred images. The useful information of these individual features indicates which image block is more focused among corresponding blocks in source images. After that, noisy features are removed using binary Genetic Grey wolf optimizer (GGWO) algorithm. The ensemble decision based on individual features is employed to fuse blurred images in the second step. The experimentation is evaluated on different multi-focus images and it reveals that GGWO based proposed method performs better visual quality than other methods.

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Authors and Affiliations

  1. Government College of Engineering, Anna University, Tirunelveli, India

    K . Sujatha

  2. Government College of Engineering, Tirunelveli, India

    D. Shalini Punithavathani

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  1. K . Sujatha
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  2. D. Shalini Punithavathani
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Correspondence to K . Sujatha.

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Sujatha, K..., Shalini Punithavathani, D. Optimized ensemble decision-based multi-focus imagefusion using binary genetic Grey-Wolf optimizer in camera sensor networks. Multimed Tools Appl 77, 1735–1759 (2018). https://doi.org/10.1007/s11042-016-4312-3

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  • DOI: https://doi.org/10.1007/s11042-016-4312-3

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