International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 05 | May -2017
p-ISSN: 2395-0072
www.irjet.net
Review Of Image Fusion Algorithm Using Different Methods Arun Kumar1, Abhishek Kumar2, Abhijeet Kumar Pandey3, Rupali Deshmukh4 1Arun
Kumar, Student, DYPIET Pimpri Abhishek Kumar, Student, DYPIET Pimpri 3Abhijeet Kumar Pandey, Student, DYPIET Pimpri 4 Rupali Deshmukh, Professor, Dept. of Electronics & Telecommunication Engineering, DYPIET Pimpri, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------2
Abstract - The main purpose of image fusion is that it
reduces the amount of data and creates new images that are more applicable for the purposes of human/machine perception. The image fusion algorithm combines information from different source images of the same scene and achieves a new image which can provide much more visual information than the original source images. This article represents various techniques by which Image Fusion Algorithm can be implemented. Key Words: Image Fusion, Algorithm, Human, Machine, Techniques.
1. INTRODUCTION The expansion in the field of sensing technologies multisensor systems have become a actuality in various fields such as remote sensing, medical imaging, machine vision and the military applications for which they were developed. The result of the use of these techniques is a increase of the amount of data available. Image fusion provides a productive way of reducing the increasing volume of information while at the same time removing out all the useful information from the source images. Multi-sensor data often presents compatible information, so image fusion provides an effective method to enable comparison and survey of data.
1.1 System Specification and Block Schematic With the high speed growth of information processing technology, we are now living in a highly informative world, and among various kind of information people obtain in their everyday life, 75% is received from vision, i.e. imaging information has already turned into a main carrier that people gain and interchange information. Thus, under large and growing demand on information data processing, how to swiftly and efficiently handle huge image data has become Š 2017, IRJET
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a major issue to be solved. As a significant branch of image processing, image fusion also develops quickly. With the eruptive growth of visual information and the rapid development of image study processing in both hardware and software fields, these achievements solidly lay a foundation of the research and application of image fusion. Owing to the ability to not only enhance the clarity of images and amount of visual information but also improve the accuracy of extraction and study of image character, image fusion is widely used in military, remote sensing, agriculture, medicine and other fields.
1.2 Hierarchial classification of Image Fusion An identified classification of image fusion divides it into 3 levels, which are pixel level fusion, feature level fusion and the decision-making level fusion. In pixel level fusion which is at the bottom of all image fusions, there is a study of images in pixels with the original image and are able to preserve more original information. The image fusion based on pixel level can produce fused image as well as providing support. The research and application based on pixel level are far more widespread and represent greater opportunity in the near term. The feature level fusion processing can provide the decisionmaking level fusion with supports. But not like the pixel level fusion, feature level fusion processing doesn’t require the high image matching metric. Besides, since the feature level fusion processing consisits of the information of characteristics, the data size has effectively diminished which leads to it is easier to compress visual information and send data. The first step of decision-making level fusion is deriving out the objective and types of several source images. Secondly, according to credibility criterion to process image after ISO 9001:2008 Certified Journal
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