Pixel-level image fusion: the case of image sequences

In pixel-level image sequence fusion, a composite image sequence has to be built of several spatially registered input image sequences. One of the primary goals in image sequence fusion is the temporal stability and consistency of the fused image sequence. To fulfill the preceding desiderata, we propose a novel approach based on a shift invariant extension of the 2D discrete wavelet transform, which yields an overcomplete and thus shift invariant multiresolution signal representation. The advantage of the shift invariant fusion method is the improved temporal stability and consistency of the fused sequence, compared to other multiresolution fusion methods. To evaluate temporal stability and consistency of the fused sequence we introduce a quality measure based on the mutual information between the inter-frame-differences (IFD) of the input sequences and the fused image sequence. If the mutual information is high, the information in the IFD of the fused sequence is low with respect to the information present in the IFDs of the input sequences, indicating a stable and consistent fused image sequence. We evaluate the performance of several multiresolution fusion schemes on a real word image sequence pair and show that the shift invariant fusion method outperforms the other multiresolution fusion methods with respect to temporal stability and consistency.

[1]  Alexander Toet,et al.  Image fusion by a ration of low-pass pyramid , 1989, Pattern Recognit. Lett..

[2]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[3]  Mongi A. Abidi,et al.  Data fusion in robotics and machine intelligence , 1992 .

[4]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[5]  Alan N. Gove,et al.  Color night vision: fusion of intensified visible and thermal IR imagery , 1995, Defense, Security, and Sensing.

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

[7]  Alexander Toet,et al.  New false color mapping for image fusion , 1996 .

[8]  G. Beylkin,et al.  On the representation of operators in bases of compactly supported wavelets , 1992 .

[9]  Thomas Fechner,et al.  Optimal fusion of TV and infrared images using artificial neural networks , 1995, SPIE Defense + Commercial Sensing.

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

[11]  Terrance L. Huntsberger,et al.  Neural Network Model For Fusion Of Visible And Infrared Sensor Outputs , 1989, Optics East.

[12]  Thierry Ranchin,et al.  Efficient data fusion using wavelet transform: the case of SPOT satellite images , 1993, Optics & Photonics.

[13]  L. W. Nichols,et al.  Conversion of infrared images to visible in color. , 1968, Applied optics.

[14]  E. Newman,et al.  The infrared 'vision' of snakes , 1982 .

[15]  Martin Beckerman,et al.  Segmentation and cooperative fusion of laser radar image data , 1994, Defense, Security, and Sensing.

[16]  Peter J. Burt,et al.  Enhanced image capture through fusion , 1993, 1993 (4th) International Conference on Computer Vision.

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