Robust Image Matching Method Based on Complex Wavelet Structural Similarity

We apply the complex wavelet structural similarity index to image matching system and propose an image matching method which has strong robustness to image transform in spatial domain. Experimental results show that the structural similarity index in complex wavelet domain reflects to a large extent structural similarity of the images compared, which is more similar to human visual cognitive system; in the meanwhile, because of approximate shift invariance of complex wavelet, this index shows good robustness to such disturbance as contrast ratio change and illumination change to template image, so it is more suitable to be used as similarity index for image matching under complex imaging conditions. Moreover, matching simulation experiment shows that this method has higher correct matching rate in complicated disturbance environment.

[1]  Zhou Wang,et al.  Complex Wavelet Structural Similarity: A New Image Similarity Index , 2009, IEEE Transactions on Image Processing.

[2]  Zhou Wang,et al.  Translation insensitive image similarity in complex wavelet domain , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[3]  Nick G. Kingsbury,et al.  A dual-tree complex wavelet transform with improved orthogonality and symmetry properties , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[4]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[5]  Quan Wen.  A Fast Image Matching Algorithm Based on Characteristic Points , 2009 .

[6]  Nick G. Kingsbury,et al.  Design of Q-shift complex wavelets for image processing using frequency domain energy minimization , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[7]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[8]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[9]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[10]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[11]  Nick Kingsbury,et al.  The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters , 1998 .

[12]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Han Ji-wan.  Digital Image Processing and Its Application , 2002 .