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

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...

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Bibliographic Details
Published in:Multimedia tools and applications 2018, Vol.77 (2), p.1735-1759
Main Authors: Sujatha, K ., Shalini Punithavathani, D.
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Experimentation
Feature extraction
Image processing
Multimedia Information Systems
Networks
Overloading
Sensors
Special Purpose and Application-Based Systems
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Summary: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-016-4312-3