Published May 30, 2022 | Version CC BY-NC-ND 4.0
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Image Fusion by a Hybrid Multiobjective Genetic Algorithm Technique

Authors/Creators

  • 1. Assistant Professor, Department of Data Structures and Algorithms, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.

Description

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.

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Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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References

  • Mantas Paulinas,AndriusUšinskas, " A Survey of Genetic Algorithms Applications for Image Enhancement And Segmentation, Information Technology and Control, vol.36, no.3, pp. 278-284, 2007.
  • Jun Kong, KaiyuanZheng, Jingbo Zhang, and XueFeng, "Multifocus image fusion using spatial frequency and genetic algorithm", IJCSNS International Journal of Computer Science and Network Security, vol. 8, no. 2, February 2008.
  • Jingbo Zhang, XueFeng, Baoling Song, Mingjie Li, and Yinghua Lu, "Multifocus image fusion using quality assessment of spatial domain and genetic algorithm", IEEE Conference on Human System Interactions, pp. 71-75, May 2008.
  • VeyselAslants, and RifatKurban, "Extending depth of field by image fusion using multiobjective genetic algorithm", IEEE conference on industrial informatics, pp. 331-336, June 2009.
  • Chaunte W Lacewell, Mohamed Gebril, Ruben Buaba, and AbdollahHomaifar, "Optimization of image fusion using genetic algorithms and discrete wavelet transform", IEEE Aerospace and Electronics Conference, pp. 116-121, July 2010.
  • Liang Hong, Kun Yang, Xianchun Pan, "Multispectral and panchromatic image fusion based on genetic algorithm and data assimilation", IEEE International Symposium on Image and Data Fusion, Aug 2011.
  • TumpaDey, "A Survey on Different Fusion Techniques of Visual and Thermal Images for Human Face Recognition", International Journal of Electronics Communication and Computer Engineering, vol. 4, issue. 6, pp. 10-14, 2013.
  • Gehad Mohamed Taher, Mohamed ElsayedWahed, Ghada E1 Taweal, and Ahmed Fouad, "Image fusion approach with noise reduction using Genetic Algorithm", International Journal of Advanced Computer Science and Applications, vol. 4, no. 11, pp. 10-16, 2013 .
  • MattiaPedergnana, Prashanth Reddy Marpu, Mauro Dalla Mura, JónAtliBenediktsson, Lorenzo Bruzzone, "A Novel Technique for Optimal Feature Selection inAttribute Profiles Based on Genetic Algorithms", IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 6, pp. 3514-3528, June 2013.
  • Junfeng Li, and Feiyan Zhang, "A novel approach to adaptive image fusion using multiobjective evolutionary algorithm", International Journal of digital content technology and its applications, vol. 7, no. 8, pp. 301-309, April 2013.
  • K. Aparna, " Retrieval of Digital Images Based on Multi-Feature Similarity using Genetic Algorithm", International Journal of Engineering Research and Applications , vol. 3, issue. 4, pp.1486-1499, Jul-Aug 2013.
  • Junfeng Li, and feiyan Zhang, "A Novel approach to adaptive image fusion using multiobjective evolutionary algorithm", International journal of digital content technology and its applications, vol7, no. 8, pp. 301-309, 2013.
  • Himanshu S. Bhatt, Samarth Bharadwaj, Richa Singh, andMayankVatsa, "Recognizing surgically altered face images using multiobjective evolutionary algorithm", IEEE transactions on information forensics and security, vol 8, no. 1, pp. 89-100, 2013.
  • Chaahat, MadhuBahl, ParveenLehana, and SantoreshKumari, " Image brightness enhacement of natural and unnatural images using continuous genetic algorithm", International journal of advanced research in computer science and software engineering, vol 3, issue 9, pp. 948-959, 2013.
  • Richa Gupta, and Deepak Awasthi, "Wavepacket image fusion technique based on genetic algorithm", IEEE 5th International Conference on Confluence the next-generation information technology, pp. 280-285, 2014.
  • Chandrashekhar G.Patil, Mahesh.T.Kolte, PrashantN.Chatur, Devendra S. Chaudhari, "Optimum Features selection by fusion using Genetic Algorithm in CBIR", International Journal on Image, Graphics and Signal Processing, pp. 25-34, 2015.
  • Suli Wang, and XiaoqingLuo, "Multi-objective optimization and gray association for multi-focus image fusion", Journal of Algorithms & Computational Technology, vol. 10, no. 2, pp. 90-98, 2016.
  • RamandeepKaur, and SukhpreetKaur, "An Approach for Image Fusion using PCA and Genetic Algorithm", International Journal of Computer Applications, vol. 145, no.6, pp. 54-59, July 2016
  • N. K. Gattim, V Rajesh, R Partheepam, S. Karunakaran, and K. N. Reddy, "Multimodal image fusion using curvelet and genetic algorithm", Journal of Scientific and Industrial Research, vol. 76, pp. 694-696, November 2017.
  • JanyShabu SL, and Jayakumar C, "Multimodal image fusion using an evolutionary-based algorithm for brain tumor detection", Biomedical Research, vol. 29, no. 14, pp. 2932-2937, 2018.
  • Muhammad ShahidFarid, ArifMahmood, Somaya Ali Al-Maadeed, "Multi-focus Image Fusion Using Content Adaptive Blurring", Information Fusion, pp. 1-17, February 2018.
  • Muhammad Arif, and Guojun Wang, "Fast curvelet transform through genetic algorithm for multimodal medical image fusion", Soft Computing, vol. 24, pp. 1815-1836, 2019
  • Xiyu Han, Tao LV, Xiangyu Song, Ting Nie, Huaidan Liang, Bin He, And ArjanKuijper, "An Adaptive Two-Scale Image Fusion of Visible and Infrared Images", IEEE access, vol.7, pp. 56341-56352, 2019.
  • Yuanmeng Zhao, YulongQiao, Cunlin Zhang, Yuejin Zhao, and Hong Wu, "Terahertz /Visible Dual-band Image Fusion Based on Hybrid Principal Component Analysis", Journal of Physics: Conference Series 1187, pp. 1-5, 2019.
  • Jing Luan, Zhong Yao, Futao Zhao, and Xin Song, "A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization", Mathematics and Computers in Simulation, vol. 156, pp. 294-309, 2019.
  • WoojinJeong, Bok Gyu Han, HyeonSeok Yang, and Young Shik Moon, "Real-Time Visible-Infrared Image Fusion using Multi-Guided Filter", KSII Transactions on Internet and Information Systems, vol. 13, no. 6, pp. 3092-3107, June 2019.
  • Jyoti S. Kulkarni, Rajankumar S. Bichkar, " Optimization in Image Fusion Using Genetic Algorithm", International Journal on Image, Graphics and Signal Processing, pp. 50-59,2019.
  • Jingming Xia, Yi Lu, and Ling Tan, "Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation", Hindawi Computational and Mathematical Methods in Medicine, vol. 2020, pp. 1-13, 2020.
  • DunbinShen, Jianjun Liu, Zhiyong Xiao, Jinlong Yang, and Liang Xiao, "A Twice Optimizing Net With Matrix Decomposition for Hyperspectral and Multispectral Image Fusion", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 4095-4110, 2020.
  • Kulkarni, J. S. and Bichkar, R. S., "Image Fusion using Hybrid Genetic Algorithm", International Journal on Emerging Technologies, 11(3), pp. 442–447, 2020.
  • Aiqing Fang, Xinbo Zhao, Jiaqi Yang, Yanning Zhang, Jiaqi Yang, and Yanning Zhang, "A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics", IEEE Transactions on Multimedia, pp. 1-13, June 2020.
  • Yu Liu, Lei Wang, Juan Cheng, Chang Li, Xun Chen, "Multi-focus Image Fusion: A Survey of the State of the Art", Information Fusion, pp. 1-27, June 2020.

Subjects

ISSN: 2277-3878 (Online)
https://portal.issn.org/resource/ISSN/2277-3878
Retrieval Number: 100.1/ijrte.A69570511122
https://www.ijrte.org/portfolio-item/a69570511122/
Journal Website: www.ijrte.org
https://www.ijrte.org/
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org/