CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Hybrid Approach of Image Fusion using Modified DTCWT with High Boost Filter Technique

by Vinay Sahu, Gagan Sharma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 5
Year of Publication: 2015
Authors: Vinay Sahu, Gagan Sharma
10.5120/20552-2929

Vinay Sahu, Gagan Sharma . A Hybrid Approach of Image Fusion using Modified DTCWT with High Boost Filter Technique. International Journal of Computer Applications. 117, 5 ( May 2015), 22-27. DOI=10.5120/20552-2929

@article{ 10.5120/20552-2929,
author = { Vinay Sahu, Gagan Sharma },
title = { A Hybrid Approach of Image Fusion using Modified DTCWT with High Boost Filter Technique },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 5 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number5/20552-2929/ },
doi = { 10.5120/20552-2929 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:58:32.479031+05:30
%A Vinay Sahu
%A Gagan Sharma
%T A Hybrid Approach of Image Fusion using Modified DTCWT with High Boost Filter Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 5
%P 22-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To achieve improve quality of image two or more images are combined using a well known process, that process is called image fusion. The fused images provide us more information than the source images. Fusion process absolutely utilizes more entirely and surplus information. This paperproposes a hybrid approach using DTCWT with High boost filtering technique and the simulation of the proposed method is done in MATLAB2012a toolbox. The analysis of our method is performing among performance measuring parameter such MSE and PSNR in which analyze that our methodology gives improved results than the existing methods.

References
  1. S. Anbumozhi, P. S. Manoharan, "Performance Analysis of Fusion Based Brain Image Classification Using Minimum Distance classifier", Journal of Theoretical and Applied Information Technology 20 April 2014. Vol. 62 No. 2, ISSN: 1992-8645.
  2. C. Morris & R. S. Rajesh, "Survey of Spatial Domain Image fusion Techniques", International Journal of Advanced Research in Computer Science Engineering and Information Technology Volume: 2 Issue: 3 08-Apr-2014, ISSN NO: 2321-3337.
  3. Kusum Rani, Reecha Sharma, "Study of Different Image fusion Algorithm", International Journal of Emerging Technology and Advanced Engineering. Website: www. ijetae. com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 5, May 2013).
  4. Pohl C. and Van Genderen J. L. , "Multi-sensor Image Fusion In Remote Sensing: Concepts, Methods And Applications", International Journal Of Remote Sensing 1998, Vol. 19, No. 5, pp. 823-854.
  5. Hui Y. X. and Cheng J. L. , 2008. "Fusion Algorithm for Remote Sensing Images Based on Non sub sampled Contour-let Transform", ACTA AUTOMATICA SINICA, Vol. 34, No. 3. pp. 274- 281.
  6. Sunil Kumar Panjeta and Deepak Sharma "A Survey on Image fusion Techniques used to Improve Image Quality", International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 7 No. 11, 2012.
  7. V. P. S. Naidu and J. R. Raol, "Pixel-level Image Fusion using Wavelets and Principal Component Analysis". Defense Science Journal, Vol. 58, No. 3, May 2008, pp. 338-352 Ó 2008, DESIDOC.
  8. MyungjinChoi , Rae Young Kim, Moon-Gyu Kim, "The Curvelet Transform For Image Fusion".
  9. Rana, A. and S. Arora, "Comparative analysis of medical image fusion". International Journal Computer Application 2013, 73:10-13. DOI: 10. 5120/12768-9371.
  10. Harbinder Singh Vinay Kumar and Sunil Bhooshan, "A Novel Approach for Detail-Enhanced Exposure Fusion Using Guided Filter", Hindawi Publishing Corporation the Scientific World Journal Volume 2014, Article ID 659217.
  11. Arya Devi P. S. and M. G. Mini, "Mammographic Image Enhancement Based on SWT and High Boost Filtering", International Journal of Computer Theory and Engineering, Vol. 7, No. 5, October 2015.
  12. V. Tsagaris, V. Anastassopoulos, and G. Lampropoulos, "Fusion of hyperspectral data using segmented PCT for enhanced color representation", IEEE Trans. Geosci. Remote Sens. , Vol. 43, No. 10, 2005, pp. 2365–2375.
  13. A. Harika, "Image Fusion Based On Dtcwt&PcaIn Presence Of Noise", International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 11, November 2014, ISSN (Online): 2278-1021.
  14. Manu V T and Philomina Simon "A Novel Statistical Fusion Rule For Image Fusion And Its Comparison In Non Sub-sampled Contourlet Transform Domain And Wavelet Domain", International Journal of Multimedia & Its Applications (IJMA) Vol. 4, No. 2, April 2012.
  15. http://in. mathworks. com/matlabcentral/answers/40897-links-to-matlab-documentation-in-published-html-pages.
Index Terms

Computer Science
Information Sciences

Keywords

HIS PCA Image fusion Wavelet transform. MATLAB

Powered by PhDFocusTM