A wavelet-based image fusion tutorial

Thumbnail Image
Full text at PDC
Publication Date
Advisors (or tutors)
Journal Title
Journal ISSN
Volume Title
Pergamon-Elsevier Science LTD
Google Scholar
Research Projects
Organizational Units
Journal Issue
The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multi scale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.
The authors wish to acknowledge Dr. L. Jañez Head of the Instituto Complutense de Imagen y Telemedicina, E. Ortiz co-worker in the same Institution, and Dr. Carreras Head of PET Institute, for his support in the medical image fusion applications. They have provided us with the medical images shown in this work. The constructive recommendations provided by the reviewers are also gratefully acknowledged.
Unesco subjects
[1] Z. Zhang, R.S. Blum, A categorization of multiscaledecomposition-based image fusion schemes with a performance study for a digital camera application, Proc. IEEE 87 (8) (1999) 1315–1326. [2] T. Ranchin, L. Wald, Fusion of high spatial and spectralresolution images: the ARSIS concept and its implementation, Photogramm. Eng. Remote Sensing 66 (1) (2000) 49–61. [3] H. Li, B.S. Manjunath, S.K. Mitra, Multisensor image fusion using the wavelet transform, Graphical Models Image Process. 57 (3) (1995) 235–245. [4] A. Rosendfeld, M. Thurston, Edge and curve detection for visual scene analysis, IEEE Trans. Comput. 20 (1971) 562–569. [5] D. Marr, Vision, Freeman, San Francisco, CA, 1982. [6] P.J. Burt, E. Adelson, The Laplacian pyramid as a compact image code, IEEE Trans. Commun. 31 (1983) 532–540. [7] E.H. Adelson, C.H. Anderson, J.R. Bergen, P.J. Burt, J. Ogden, Pyramid methods in image processing, RCA Eng. 29 (6) (1984) 33–41. [8] T. Lindeberg, Scale-Space Theory in Computer Vision, Kluwer, Norwell, MA, 1994. [9] B. Garguet-Duport, J. Girel, J. Chassery, J.G. Pautou, The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data, Photogramm. Eng. Remote Sensing 62 (9) (1996) 1057–1066. [10] J.B.A. Maintz, M.A. Viergever, A survey of medical image registration, Med. Image Anal. 2 (1) (1998) 1–36. [11] R.J. Althof, M.G.J. Wind, J.T. Dobbins, A rapid and automatic image registration algorithm with subpixel accuracy, IEEE Trans. Med. Imaging 16 (3) (1997) 308–316. [12] C. Schmid, R. Mohr, C. Bauckhage, Evaluation of interest points, Int. J. Comput. Vision 37 (2) (2000) 151–172. [13] J.L. Starck, F. Murtagh, A. Bijaoui, Image Processing and Data Analysis: the Multiscale Approach, Cambridge, University Press, Cambridge, 2000. [14] E.J. Stollnitz, T.D. DeRose, D.H. Salesin, Wavelets for computer graphics: a primer, part 1, IEEE Comput. Graphics Appl. 15 (3) (1995) 76–84. [15] I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, 1992. [16] I. Daubechies, Orthonormal bases of compactly supported wavelets, Commun. Pure Appl. Math. 41 (1988) 909–996. [17] D.M. Tsai, B. Hsiao, Automatic surface inspection using wavelet reconstruction, Pattern Recognition 34 (2001) 1285–1305. [18] M. Misiti, Y. Misiti, G. Oppenheim, J. Poggi, Wavelet Toolbox for Use with MATLAB: User’s Guide, The MathWorks, Natick, NA, 2000. [19] C.S. Burrus, R.A. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms: a Primer, Prentice-Hall, Upper Saddle River, NJ, 1998. [20] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989) 674–693. [21] E. Morales, F.Y. Shih, Wavelet coeIcients clustering using morphological operations and pruned quadtrees, Pattern Recognition 33 (2000) 1611–1620. [22] A.M. Eskicioglu, P.S. Fisher, Image quantity measures and their performance, IEEE Trans. Commun. 43 (12) (1995) 2959–2965. [23] S. Li, J.T. Kwok, Y. Wang, Combination of images with diverse focuses using the spatial frequency, Inf. Fusion 2 (2001) 169–176. [24] A. Mojsilovic, M.V. Popovic, A.N. Neskovic, A.D. Popovic, Wavelet image extension for analysis and classification of infarted myocardial tissue, IEEE Trans. Biomed. Eng. 44 (9) (1997) 856–866. [25] V. Petrovic, C. Xydeas, Multiresolution image fusion using cross band selection, Proc. SPIE 39 (1999) 319–326. [26] I. Bloch, Information combination operators for data fusion: a comparative review with classification, IEEE Trans. Systems Man Cybernet. 26 (1) (1996) 52–67. [27] R. Welch, M. Ehlers, Merging multiresolution SPOT HRV and landsat TM Data, Photogramm. Eng. Remote Sensing 53 (3) (1987) 301–303. [28] B.J. Matuszewski, L.K. Shark, J.P. Smith, M.R. Varley, Automatic fusion of multiple non-destructive testing images and CAD models for industrial inspection, Proceedings of the IEE Seventh International Conference on Image Processing and its Applications, IPA’99, Manchester, 1999. pp. 661–665. [29] D.A. Yocky, Image merging and data fusion by means of the discrete two-dimensional wavelet transform, J. Opt. Soc. Am. A: Opt., Image Sci. Vision 12 (9) (1995) 1834–1841. [30] J. Nuñez, X. Otazu, O. Fors, A. Prades, Simultaneous image fusion and reconstruction using wavelets applications to SPOT + LANDSAT images, Vistas Astron. 41 (3) (1997) 351–357. [31] R.C. Gonzalez, R.E. Woods, Digital Image Processing, Addison-Wesley, Reading, MA, 1993. [32] S.G. Nikolov, D.R. Bull, C.N. Canagarajah, M. Halliwell, P.N.T. Wells, Fusion of 2-D images using their multiscale edges, Proceedings of the International Conference on Pattern Recognition, 2000, pp. 1051–1054. [33] H. Greenspan, C.H. Anderson, S. Akber, Image enhancement by nonlinear extrapolation in frequency space, IEEE Trans. Image Process. 9 (6) (2000) 1035–1048. [34] O. Rockinger, Image sequence fusion using a shift-invariant wavelet transform, Proceedings of the IEEE International Conference on Image Processing, Vol. III, 1997, pp. 288–291. [35] A. Toet, Image fusion by a ratio of low-pass pyramid, Pattern Recognition Lett. 9 (4) (1989) 245–253. [36] A. Toet, A morphological pyramidal image decomposition, Pattern Recognition Lett. 9 (4) (1989) 255–261. [37] S. Li, J.T. Kwok, Y. Wang, Multifocus image fusion using artificial neural networks, Pattern Recognition Lett. 23 (2002) 985–997. [38] Rockinger’s MATLAB toolbox in www.rockinger. [39] L. Wald, T. Ranchin, M. Mangolini, Fusion of satellite images of di4erent spatial resolutions, Photogramm. Eng. Remote Sensing 63 (6) (1997) 691–699. [40] D.P. Filiberti, S.E. Marsh, R.A. Schwengerdt, Synthesis of imagery with high spatial and spectral resolution from multiple image sources, Opt. Eng. 33 (8) (1994) 2520–2528. [41] S. de Bethune, F. Muller, M. Binard, Adaptive intensity matching filters: a new tool for multi-resolution data fusion, Proceedings of the Multi-Sensor Systems and Data Fusion for Telecommunications, Remote Sensing and Radar, RTO-NATO Organization, 1997, pp. 671–680. [42] J. Hill, C. Diemer, O. StWover, T. Udelhoven, A local correlation approach for the fusion of remote sensing data with diferent spatial resolutions in forestry applications, Proceedings of the International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3, 1999, pp. 781–789. [43] R.A. Showengerdt, Reconstruction of multispatial, multispectral image data using spatial frequency contents, Photogramm. Eng. Remote Sensing 46 (10) (1980) 1325–1334. [44] F.J. Tapiador, J.L. Casanova, An algorithm for the fusion of images based on Jaynes’ maximum entropy method, Int. J. Remote Sensing 23 (4) (2002) 777–785. [45] A. Toet, L. Ruyven, J. Velaton, Merging thermal and visual images by a contrast pyramid, Opt. Eng. 28 (7) (1989) 789–792. [46] J. Zhou, D.L. Civco, J.A. Silander, A wavelet transform method to merge Landsat TM and SPOT panchromatic data, Int. J. Remote Sensing 19 (4) (1998) 743–757. [47] S. Li, J.T. Kwok, Y. Wang, Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images, Inf. Fusion 3 (2002) 17–23. [48] M. Santos, G. Pajares, M. Portela, J.M. de la Cruz, in: F.J. Perales, A.J.C. Campilho, N. Pérez de la Blanca, A. Sanfeliu (Eds.), A new Wavelets Image Fusion Strategy, Lecture Notes in Computer Science, Pattern Recognition and Image Analysis, Vol. 2652, Springer, Berlin, 2003, pp. 919–926. [49] G. Pajares, J.M. de la Cruz, Visi*on por Computador: Imágenes Digitales y Aplicaciones, RA-MA, Madrid, 2001.