Open Access   Article Go Back

Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid

B. Asha Latha1 , M.Babu Reddy2

  1. Computer Science, KrishnaUniversity, Machilipatnam, India.
  2. Computer Science, KrishnaUniversity, Machilipatnam, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 403-407, Mar-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i3.403407

Online published on Mar 30, 2018

Copyright © B. Asha Latha, M.Babu Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: B. Asha Latha, M.Babu Reddy, “Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.403-407, 2018.

MLA Style Citation: B. Asha Latha, M.Babu Reddy "Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid." International Journal of Computer Sciences and Engineering 6.3 (2018): 403-407.

APA Style Citation: B. Asha Latha, M.Babu Reddy, (2018). Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid. International Journal of Computer Sciences and Engineering, 6(3), 403-407.

BibTex Style Citation:
@article{Latha_2018,
author = {B. Asha Latha, M.Babu Reddy},
title = {Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2018},
volume = {6},
Issue = {3},
month = {3},
year = {2018},
issn = {2347-2693},
pages = {403-407},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1817},
doi = {https://doi.org/10.26438/ijcse/v6i3.403407}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i3.403407}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1817
TI - Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid
T2 - International Journal of Computer Sciences and Engineering
AU - B. Asha Latha, M.Babu Reddy
PY - 2018
DA - 2018/03/30
PB - IJCSE, Indore, INDIA
SP - 403-407
IS - 3
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
655 348 downloads 218 downloads
  
  
           

Abstract

In the technically advanced world image fusion attracts as a considerable assistant for image processing experts. The role of image fusion in processing of images is robust by extracting the best and complementary features from two or more images and integrating that information by using appropriate algorithm in order to provide better recognition characteristics. Image fusion experts have been using images for a long time with machine learning algorithms. It requires very intensive pre-processing steps. Recently experts are very much interested in using long existing deep learning algorithms in processing the image data. This paper presents the deep convolutional neural network based image fusion using Laplacian pyramid method. Firstly the paper concentrates on the existing image fusion techniques and related work. Secondly on convolutional neural networks, deep learning and their features. Thirdly it presented the similarities among Convolutional Neural Network, Gaussian pyramid, Laplacian pyramid models. Lastly our proposed method and discussion on experimental results. It was observed that Deep Convolutional Neural Network and Laplacian pyramid based image fusion method gave better PSNR Values than the existing Laplacian Pyramid fusion methods for various images.

Key-Words / Index Term

Image Fusion, Deep Learning, Convolutional Neural Network, Laplacian Pyramid

References

[1]. A Goshtasby, S. Nikolov, Image Fusion: advances in the state of the art, Inf.Fusion 8(2) (2007) 114-118.
[2]. S. Li, X.Kang, L. Fang,J.Hu,H.Yin, pixel-level image fusion: a survey of the state of the art, Inf. Fusion 33(2017)100-112.
[3].A review: Image Fusion Techniques and Applications by Mamta Sharma.
[4]. Deep Learning for pixel-level image fusion: Recent advances and future prospects by Yu Liu, Xun Chen, Zengfu Wang, Z. Jane Wang, Rabab K. Ward, Xuesong Wang.
[5]. Y. Liu, X.Chen, H. Peng, Z.Wang, Multi-focus image fusion with deep convolutional neural network, Inf. Fusion 36(2017) 191-207.
[6].B. Yang, J. Zhong Y.Li, Z.Chen, Multi-focus image fusion and super-resolution with convolutional neural network, Int. J. Wavelets Multiresolut.Inf. Process.15(4)(2017)1750037:1-15.
[7]. C.Du, S.Gao, Image segmentation-based multi-focus image fusion through multi-scale convolutional neural network, IEEE access 5 (2017) 15750-15761.
[8]. N. Kalantari, R. Ramamoorthi, Deep high dynamic range Of dynamic scenes, ACM Trans. Graph, 36 (4) (2017)144:1-12.
[9]. Y.Liu, X.Chen, J. Cheng, H.Peng, A medical Image fusion method based on convolutional neural network, Proceedings of 20th International Conference on Information Fusion,(2017),pp.1-7.
[10]. W. Huang, L. Xiao, Z. Wei, H. Liu, S.Tang, A new pan-sharpening method with deep neural networks, IEEE Geosci. Remote Sens. Lett.12(5)(2015)1037-1041.
[11]. Z.Zhang, R. Blum, A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application, Proc. IEEE 87(8)(1999) 1315-1326.
[12]. G. Piella, A general framework for multiresolution image fusion:from pixels to regions, Inf. Fusion 4 (4)(2003) 259-280.
[13]. Implementation of Image Fusion algorithm using MATLAB(LAPLACIAN PYRAMID) by M. Pradeep,Assoc.Professor,ECE Dept,Shri Vishnu Engineering College for Women.