CFP last date
20 December 2024
Reseach Article

Multi-Focus Image Fusion in Transform Domain using Steerable Pyramids

by Kiranjeet Kaur, Gurinder Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 5
Year of Publication: 2016
Authors: Kiranjeet Kaur, Gurinder Singh
10.5120/ijca2016908826

Kiranjeet Kaur, Gurinder Singh . Multi-Focus Image Fusion in Transform Domain using Steerable Pyramids. International Journal of Computer Applications. 138, 5 ( March 2016), 14-20. DOI=10.5120/ijca2016908826

@article{ 10.5120/ijca2016908826,
author = { Kiranjeet Kaur, Gurinder Singh },
title = { Multi-Focus Image Fusion in Transform Domain using Steerable Pyramids },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 5 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number5/24374-2016908826/ },
doi = { 10.5120/ijca2016908826 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:51.226862+05:30
%A Kiranjeet Kaur
%A Gurinder Singh
%T Multi-Focus Image Fusion in Transform Domain using Steerable Pyramids
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 5
%P 14-20
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In optical lenses of conventional cameras, depth of field is restricted to a particular range. Therefore, only those objects that are at a particular distance from the camera captured clearly with proper focus whereas objects at other distances in front of or behind the focus plane remain defocused and blurred. However, for accurately interpreting and analyzing images, it is desired to obtain images with every object in focus. Multi-focus image fusion is an effective technique to solve this problem by combining two or more images of the same scene taken with different focus settings into a single all-in-focus image with extended depth of field, which is very useful for human or machine perception. The main drawbacks of pixel based fusion methods are misalignment of decision map with boundary of focused objects and wrong decision in sub-regions of the focused or defocused regions which produce undesirable artifacts in the final fused image. Therefore frequency domain methods are more preferred then spatial domain methods. In previous years, many kinds of multi-scale transforms have been proposed and adopted for image fusion such as pyramid decomposition, discrete wavelet transform (DWT) , dual-tree complex wavelet transform (DTCWT) , and discrete cosine harmonic wavelet transform (DCHWT). These transforms are widely used as wavelets become dominant filters in most of the techniques but they still have drawbacks. Wavelets are the lack of translation invariance, especially in two- dimensional (2D) signals and the poor selectivity in orientation. This can be overcome by steerable pyramid transform as one can choose the orientation before applying the filters. We have applied this method to achieve multi-focus image fusion of images which have different focus areas while capturing them. Experimental results shows that the proposed method affectively carried out fusion process as performance of the technique has been evaluated by various parameters namely mutual information, QABF factor used for edge perservance measuring and entropy etc.

References
  1. T. Wan, C. Zhu, Z. Qin; ”Multi-focus image fusion based on robust principal component analysis,” Pattern Recogn. Lett. 34 (9) (2013) 1001–1008.
  2. A. Saha, G. Bhatnagar, Q.M.J. Wu, “Mutual spectral residual approach for multi-focus image fusion, “ Digit. Signal Process. 23 (4) (2013) 1121–1135.
  3. I. De, B. Chanda, Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure, Inf. Fusion 14 (2) (2013) 136–146.
  4. S. Li, B. Yang, Multifocus image fusion using region segmentation and spatial frequency, Image Vis. Comput. 26 (7) (2008) 971–979.
  5. L. Chen, J. Li, C.L.P. Chen, Regional multifocus image fusion using sparse representation, Opt. Express 21 (4) (2013) 5182–5197.
  6. I. De, B. Chanda, Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure, Inf. Fusion 14 (2) (2013) 136–146.
  7. RichaSrivastava, Om Prakash, AshishKhare, “Biorthogonal wavelet transform based image fusion using absolute maximum fusion rule” Information & Communication Technologies (ICT), 2013 IEEE Conference April 2013 Page(s): 577 – 582.
  8. Guofeng Shao; Lixin Liu,” An Effective Wavelet-based Scheme for Multi-focus Image Fusion” Published in Mechatronics and Automation (ICMA),” 2013 IEEE International Conference Aug. 2013 Page(s): 1720 – 1725.
  9. Xu Cao; Huaxun Zhang, “A Way of Image Fusion Based on Wavelet Transform” Published in Mobile Ad-hoc and Sensor Networks (MSN), 2013 IEEE Ninth International Conference on Date: 11-13 Dec. 2013 Page(s): 498 – 501.
  10. Xiuqin Su; Fan Xu,” An Enhanced Infrared and visible Image Fusion Method Based on Wavelet Transform” Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 International Conference held on Date: 26-27 Aug. 2013 Page(s): 453 – 456.
  11. C. Xydeas, V. Petrovic, Objective image fusion performance measure, Electron.Lett. 36 (4) (2000) 308–309.
Index Terms

Computer Science
Information Sciences

Keywords

Image fusion gray scale images multi-focus pyramid transforms wavelets