Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter

Yong Jiang; Minghui Wang

Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter

View Fulltext

Author(s): Yong Jiang ^{1, 2} and Minghui Wang ²
- Affiliations: 1: College of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, People's Republic of China;
  2: College of Computer Science, Sichuan University, Chengdu 610064, People's Republic of China
Source: Volume 8, Issue 3, March 2014, p. 183 – 190
DOI: 10.1049/iet-ipr.2013.0429 , Print ISSN 1751-9659, Online ISSN 1751-9667

Received 19/07/2012, Accepted 02/11/2013, Revised 17/10/2013, Published 10/02/2014

For pixel-level image fusion, the edges of the source images should be as much as possible integrated into the fused image because the human visual system is sensitive to them. In this study, the authors utilise the multiscale edge-preserving decomposition (MSEPD) based on the weighted least squares filter to fuse the source images. In the authors’ method, first, the source images are decomposed by the MSEPD into a base image and a series of detail images, respectively. Then, the detail images of same scale are combined via the different fusion rules designed for different kinds of source images; the base images are combined via the average-value rule. Finally, the fused image is constructed by adding the fused base image and detail images together. The proposed fusion method is verified on several kinds of images and compared with some methods based on multiscale decomposition. The experimental results indicate that the proposed method can provide better fused images, meanwhile manifesting a good edge-preserving performance.

References

1. 1)
  - C. Xydeas , V. Petrovic . Objective image fusion performance measure. Electron. Lett. , 308 - 309
2. 2)
  - 1. Goshtasby, A.A., Nikolov, S.: ‘Image fusion: advances in the state of the art’, Inf. Fusion, 2007, 8, (2), pp. 114–118 (doi: 10.1016/j.inffus.2006.04.001).
3. 3)
  - 31. Tessens, L., Ledda, A., Pizurica, A., Philips, W.: ‘Extending the depth of field in microscopy through curvelet-based frequency-adaptive image fusion’. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Honolulu, Hawaii, USA, April 2007, pp. 861–864.
4. 4)
  - 18. http://www.cs.huji.ac.il/danix/epd/wlsfilter.m, accessed January 2013.
5. 5)
  - A.M. Eskicioglu , P.S. Fisher . Image quality measures and their performance. IEEE Trans. Commun. , 12 , 2959 - 2965
6. 6)
  - C. Pohl , J.L.V. Genderen . Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens. , 5 , 823 - 854
7. 7)
  - 20. Huang, W., Jing, Z.: ‘Evaluation of focus measures in multi-focus image fusion’, Pattern Recognit. Lett., 2007, 28, (4), pp. 493–500 (doi: 10.1016/j.patrec.2006.09.005).
8. 8)
  - 16. Zhao, J., Feng, H., Xu, Z., Li, Q., Liu, T.: ‘Detail enhanced multi-source fusion using visual weight map extraction based on multi scale edge preserving decomposition’, Opt. Commun., 2013, 287, pp. 45–52 (doi: 10.1016/j.optcom.2012.08.070).
9. 9)
  - P.J. Burt , E.H. Adelson . The Laplacian Pyramid as a compact image code. IEEE Trans. Commun. , 4 , 532 - 540
10. 10)
  - 4. Rockinger, O.: ‘Pixel level fusion of image sequences using wavelet frames’. Proc. 16th Leeds Annual Statistical Research Workshop, Leeds, UK, July 1996, pp. 149–154.
11. 11)
  - 8. Tomasi, C., Manduchi, R.: ‘Bilateral filtering for gray and color images’. Proc. Int. Conf. on Computer Vision, Bombay, India, January 1998, pp. 839–846.
12. 12)
  - Z. Zhang , R.S. Blum . A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proc. IEEE , 8 , 1315 - 1326
13. 13)
  - J. Canny . A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 679 - 697
14. 14)
  - 26. Wang, W., Tang, P., Zhu, C.: ‘A wavelet transform based image fusion method’, J. Image Graph., 2001, 6, (11), pp. 1130–1136.
15. 15)
  - 6. Chen, C., Wang, C.-D.: ‘A simple edge-preserving filtering technique for constructing multi-resolution systems of images’, Pattern Recognit. Lett., 1999, 20, (5), pp. 495–506 (doi: 10.1016/S0167-8655(99)00017-3).
16. 16)
  - 29. Rockinger, O.: ‘Image sequence fusion using a shift-invariant wavelet transform’. Proc. of Int. Conf. on Image Processing, Washington, DC, USA, October 1997, pp. 288–291.
17. 17)
  - 32. Ren, S., Cheng, J., Li, M.: ‘Multiresolution fusion of PAN and MS images based on the curvelet transform’. Proc. IEEE Int. Conf. on Geoscience and Remote Sensing, Honolulu, Hawaii, USA, July 2010, pp. 472–475.
18. 18)
  - 33. Tang, L., Zhao, F., Zhao, Z.-G.: ‘The nonsubsampled contourlet transform for image fusion’. Proc. of Int. Conf. on Wavelet Analysis and Pattern Recognition, Beijing, China, November 2007, pp. 305–310.
19. 19)
  - H. Sheikh , A. Bovik . Image information and visual quality. IEEE Trans. Image Process. , 2 , 430 - 444
20. 20)
  - G. Pajares , J.M. Cruz . A wavelet-based image fusion tutorial. Pattern Recognit. , 9 , 1855 - 1872
21. 21)
  - 17. Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: ‘Interactive local adjustment of tonal values’, ACM Trans. Graph., 2006, 25, pp. 646–653 (doi: 10.1145/1141911.1141936).
22. 22)
  - G. Qu , D. Zhang , P. Yan . Information measure for performance of image fusion. Electron. Lett. , 313 - 315
23. 23)
  - 14. Hu, J., Li, S.: ‘The multiscale directional bilateral filter and its application to multisensor image fusion’, Inf. Fusion, 2012, 13, (3), pp. 196–206 (doi: 10.1016/j.inffus.2011.01.002).
24. 24)
  - 9. Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: ‘Edge-preserving decompositions for multi-scale tone and detail manipulation’, ACM Trans. Graph., 2008, 27, (3), pp. 1–10 (doi: 10.1145/1360612.1360666).
25. 25)
  - 5. Chung, K.-L., Yang, W.-J., Yan, W.-M.: ‘Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation’, J. Vis. Commun. Image Represent., 2008, 19, (5), pp. 299–310 (doi: 10.1016/j.jvcir.2008.02.002).
26. 26)
  - H. Li , B.S. Manjunath , S.K. Mitra . Multisensor image fusion using the wavelet transform. Graph. Models Image Process. , 235 - 245
27. 27)
  - 10. Xu, L., Lu, C., Xu, Y., Jia, J.: ‘Image smoothing via L0 gradient minimization’, ACM Trans. Graph., 2011, 30, (6), pp. 174:1–174:12.
28. 28)
  - P. Perona , J. Malik . Scale-space and edge detection using anistropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. , 7 , 629 - 639
29. 29)
  - N. Mitianoudis , T. Stathaki . Image fusion schemes using ICA bases. Inf. Fusion , 131 - 142
30. 30)
  - A.L. Cunha , J. Zhou , M.N. Do . “The nonsubsampled contourlet transform: theory, design and applications”. IEEE Trans. Image Process , 10 , 3089 - 3101
31. 31)
  - J.J. Lewis , R.J. Ocallaghan , S.G. Nikolov , D.R. Bull , N. Canagarajah . Pixel- and region-based image fusion with complex wavelets. Inf. Fusion , 119 - 130
32. 32)
  - S. Nayar , Y. Nakagawa . Shape from focus. IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 824 - 831
33. 33)
  - G. Piella . A general framework for multiresolution image fusion: from pixels to regions. Inf. Fusion , 4 , 259 - 280

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter

References

Related content