Abstract
Image Fusion is applied to get back a group of data from two or more than two images and put it into one image to create additional wealthy information and profitable more than any of the input data that led to increase the features and performance of information. The quality of the resultant data relies on the implementation of the process. Image fusion is excessively utilized in stereo camera fusion, medical application, manufacture process monitoring, electronic circuit design and inspection, complex machine diagnostics and in intelligent robots on assembly lines. This study displays a literature review on different types of algorithm and theories which apply on images. Many quality criteria have debated to do a brief comparison of these methods. The applications of image fusion are showed in this paper.
Highlights
The main purpose of this study is giving a guide for any specialist who wants to work on image fusion
Principal component analysis (PCA), High Pass Filter Technique (HPF) and Brovey are simple, easy for computational and fast algorithms but these techniques result in colour distortion
Discrete Cosine Transform (DCT) method is used in real time system but cannot be applied with block size less than 8×8
Summary
Image fusion Technique takes excessive image fusion algorithms begins to play an and complementary information from the input important role in many application such as remote signals "more than one image "to produce an sensing[2,3,4,5] which are depending on enhancement output signal "one image " which is further suitable in hyperspectral imagery [6] and deep convolution for the aim of human observation. The accuracy neural network [7,8] Another major application of and all information of the resultant output are image fusion is in the medical and biological raised. Image fusion applications are depending on image fusion to technologies have greatly developed the accuracy improve the quality of output [17]. Analysis can be used for executing a fusion in a series of images [38]
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