P–M equation based multiscale decomposition and its application to image fusion

P–M equation proposed by Perona and Malik can not only perform scale-space, but also preserve edges while smoothing an image. In this paper, we employ this property to construct a new multiscale decomposition method, by which an image can be decomposed into a sequence of detail images and a base image, and the initial image can be perfectly reconstructed by adding up these decomposed images. This decomposition method is applied to multisensor image fusion. The source images are first decomposed into the detail images and the base image. Then, these images are combined according to the given fusion rules. Finally, the fused image is reconstructed by adding up the fused detail images and base image. Compared with conventional methods based on multiscale decomposition, experimental results over multifocus images, visible and infrared images, and medical images demonstrate the superiority of our method in terms of visual inspection and objective measures.

[1]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[2]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[3]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[4]  Alexander Toet,et al.  Hierarchical image fusion , 1990, Machine Vision and Applications.

[5]  James J. Clark.  Singularity Theory and Phantom Edges in Scale Space , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Qiang Chen,et al.  Ramp preserving Perona-Malik model , 2010, Signal Process..

[7]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Feng Zhao,et al.  The nonsubsampled contourlet transform for image fusion , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[9]  Max A. Viergever,et al.  Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

[10]  Vladimir S. Petrovic,et al.  Gradient-based multiresolution image fusion , 2004, IEEE Transactions on Image Processing.

[11]  Max A. Viergever,et al.  A General Framework for Geometry-Driven Evolution Equations , 1997, International Journal of Computer Vision.

[12]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Oliver Rockinger,et al.  Image sequence fusion using a shift-invariant wavelet transform , 1997, Proceedings of International Conference on Image Processing.

[14]  Shutao Li,et al.  The multiscale directional bilateral filter and its application to multisensor image fusion , 2012, Inf. Fusion.

[15]  M. Beaulieu,et al.  Multi- spectral image resolution refinement using stationary wavelet transform , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[16]  Alexander Toet,et al.  A morphological pyramidal image decomposition , 1989, Pattern Recognit. Lett..

[17]  Ching-Lai Hwang,et al.  A new approach for multiple objective decision making , 1993, Comput. Oper. Res..

[18]  Henk J. A. M. Heijmans,et al.  A new quality metric for image fusion , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[19]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[20]  Cedric Nishan Canagarajah,et al.  Pixel- and region-based image fusion with complex wavelets , 2007, Inf. Fusion.

[21]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[22]  G. Qu,et al.  Information measure for performance of image fusion , 2002 .

[23]  Andrew P. Witkin,et al.  Scale-space filtering: A new approach to multi-scale description , 1984, ICASSP.

[24]  Aleksandra Pizurica,et al.  Extending the Depth of Field in Microscopy Through Curvelet-Based Frequency-Adaptive Image Fusion , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[25]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[26]  Gonzalo Pajares Martinsanz,et al.  A wavelet-based image fusion tutorial , 2004 .

[27]  Stavri G. Nikolov,et al.  Image fusion: Advances in the state of the art , 2007, Inf. Fusion.

[28]  Jian Cheng,et al.  Multiresolution fusion of Pan and MS images based on the Curvelet transform , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[29]  Philippe Montesinos,et al.  Author ' s personal copy Ramp preserving Perona – Malik model , 2010 .

[30]  B. S. Manjunath,et al.  Multi-sensor image fusion using the wavelet transform , 1994, Proceedings of 1st International Conference on Image Processing.