Introduction of a Symmetric Tight Wavelet Frame to Image Fusion Methods Based on Substitutive Wavelet Decomposition

A useful technique in various applications of remote sensing involves the fusion of panchromatic and multispectral images. Wavelet-based approaches to image fusion generally produce high-quality spectral content in fused images. However, the spatial resolution obtained by most wavelet-based methods is less than that obtained by the intensity-hue-saturation (IHS) method. Recent studies show that if an undecimated discrete wavelet transform (DWT) is used for image fusion, the spatial resolution of the fused images can be as good as that of images obtained by the IHS method. This effect occurs because an undecimated DWT is exactly shift-invariant. In this paper, the author introduces a symmetric tight wavelet frame to image fusion methods that are based on substitutive wavelet decomposition. The introduced tight wavelet frame transform is nearly shift-invariant with desired properties such as wavelet smoothness, short support, and symmetry. The experimental results show the possibility as an alternative DWT approach for image fusion. In addition, the author proposes a fast algorithm for an improved IHS method introduced by González-Aud́ıcana et al. The proposed approach enables a fast, easy, and extendable implementation. Hence, for the fusion of IKONOS panchromatic and multispectral images, the near-infrared band of IKONOS may be included in the definition of the intensity component. This approach produces satisfactory results, both visually and quantitatively.

[1]  Myeong-Ryong Nam,et al.  Fusion of multispectral and panchromatic Satellite images using the curvelet transform , 2005, IEEE Geoscience and Remote Sensing Letters.

[2]  Amrane Houacine,et al.  Redundant versus orthogonal wavelet decomposition for multisensor image fusion , 2003, Pattern Recognit..

[3]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[4]  P. Dutilleux An Implementation of the “algorithme à trous” to Compute the Wavelet Transform , 1989 .

[5]  Truong Q. Nguyen,et al.  Lattice structure for regular paraunitary linear-phase filterbanks and M-band orthogonal symmetric wavelets , 2001, IEEE Trans. Signal Process..

[6]  J. Schott,et al.  Resolution enhancement of multispectral image data to improve classification accuracy , 1993 .

[7]  Yun Zhang,et al.  Understanding image fusion , 2004 .

[8]  Qingtang Jiang,et al.  Parameterizations of Masks for Tight Affine Frames with Two Symmetric/Antisymmetric Generators , 2003, Adv. Comput. Math..

[9]  I. Selesnick,et al.  Symmetric wavelet tight frames with two generators , 2004 .

[10]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  C. Eddie Moxey,et al.  Hypercomplex correlation techniques for vector images , 2003, IEEE Trans. Signal Process..

[12]  Te-Ming Tu,et al.  A new look at IHS-like image fusion methods , 2001, Inf. Fusion.

[13]  L. Wald,et al.  Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation , 2000 .

[14]  Xavier Otazu,et al.  Multiresolution-based image fusion with additive wavelet decomposition , 1999, IEEE Trans. Geosci. Remote. Sens..

[15]  A. S. Solodovnikov,et al.  Hypercomplex Numbers: An Elementary Introduction to Algebras , 1989 .

[16]  Te-Ming Tu,et al.  A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery , 2004, IEEE Geoscience and Remote Sensing Letters.

[17]  Martin Vetterli,et al.  Oversampled filter banks , 1998, IEEE Trans. Signal Process..

[18]  O. Herrmann Design of nonrecursive digital filters with linear phase , 1970 .

[19]  Stephen J. Sangwine,et al.  Colour image filters based on hypercomplex convolution , 2000 .

[20]  C. Chui,et al.  Compactly supported tight frames associated with refinable functions , 2000 .

[21]  I. Daubechies,et al.  Framelets: MRA-based constructions of wavelet frames☆☆☆ , 2003 .

[22]  Y. Chibani,et al.  The joint use of IHS transform and redundant wavelet decomposition for fusing multispectral and panchromatic images , 2002 .

[23]  C. Chui,et al.  Compactly supported tight and sibling frames with maximum vanishing moments , 2001 .

[24]  Todor Cooklev,et al.  Maximally flat FIR filters , 1993, 1993 IEEE International Symposium on Circuits and Systems.

[25]  Ivan W. Selesnick,et al.  Symmetric nearly shift-invariant tight frame wavelets , 2005, IEEE Transactions on Signal Processing.

[26]  Myungjin Choi,et al.  A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter , 2006, IEEE Trans. Geosci. Remote. Sens..

[27]  Zuowei Shen Affine systems in L 2 ( IR d ) : the analysis of the analysis operator , 1995 .

[28]  Xavier Otazu,et al.  Comparison between Mallat's and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images , 2005 .

[29]  I. Daubechies Ten Lectures on Wavelets , 1992 .

[30]  A. Ron,et al.  Affine Systems inL2(Rd): The Analysis of the Analysis Operator , 1997 .

[31]  J. Zhou,et al.  A wavelet transform method to merge Landsat TM and SPOT panchromatic data , 1998 .

[32]  Rafael García,et al.  Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[33]  L. Wald,et al.  Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .

[34]  Helmut Bölcskei,et al.  Frame-theoretic analysis of oversampled filter banks , 1998, IEEE Trans. Signal Process..

[35]  Petukhov,et al.  Constructive Approximation Symmetric Framelets , 2003 .