Image Fusion by a Hybrid Multiobjective Genetic Algorithm Technique
Jyoti S. Kulkarni

Dr. Jyoti S. Kulkarni*, Assistant Professor, Department of Data Struc-tures and Algorithms, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.

Manuscript received on 21 April 2022. | Revised Manuscript received on 29 April 2022. | Manuscript published on 30 May 2022 | PP: 123-128 | Volume-11 Issue-1, May 2022. | Retrieval Number: 100.1/ijrte.A69570511122 | DOI: 10.35940/ijrte.A6957.0511122
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Sensors used in image acquisition. This sensor technology is going on upgrading as per user need or as per need of an application. Multiple sensors collect the information of their respective wavelength band. But one sensor is not sufficient to acquire the complete information of one scene. To gain the overall data of one part, it becomes essential to cartel the images from multiple sources. This is achieved through merging. It is the method of merging the data from dissimilar input sources to create a more informative image compared with an image from a single input source. These are multisensor photos e.g. panchromatic and multispectral images. The first image offers spatial records whereas the lateral image offers spectral data. Through visible inspections, the panchromatic photo is clearer than a multispectral photo however the grey shade image is. Articles are greater clear however now not recognized whereas multispectral picture displays one of a kind shades however performing distortion. So comparing the characteristics of these two images, the resultant image is greater explanatory than these enter images. Fusion is done using different transform methods as well as the genetic algorithm. Comparing the results obtained by these methods, the output image by the genetic algorithm is clearer. The feature of the resultant image is verified through parameters such as root mean square error, peak signal to noise ratio, mutual information, and spatial frequency. In the subjective analysis, some transform techniques also giving exact fused images. The hybrid approach combines the transform technique and a genetic algorithm is used for image fusion. This is again compared with genetic algorithm results. The same performance parameters are used. And it is observed that the hybrid genetic algorithm is superior to the genetic algorithm. Here the only root means square error parameter is considered under the fitness function of the genetic algorithm so only this parameter is far better than the remaining parameters. If we consider all parameters in the fitness function of the genetic algorithm then all parameters using a hybrid genetic algorithm will give better performance. This method is called a hybrid multiobjective genetic algorithm. 
Keywords: Genetic Algorithm, Hybrid Multiobjec Tive Genetic Algorithm, Image Fusion
Scope of the Article: Sequential, Parallel and Distributed Algorithms and Data Structures