Application of Image Fusion Using Wavelet Transform In Target Tracking System

The abstract The fusion of images is the process of combining two or more images into a single image retaining important features from each. Fusion is an important technique within many disparate fields such as remote sensing, robotics and medical applications. Wavelet based fusion techniques have been reasonably effective in combining perceptually important image features. Shift invariance of the wavelet transform is important in ensuring robust sub-band fusion. Therefore, the novel application of the shift invariant and directionally selective Dual Tree Complex Wavelet Transform (DT-CWT) to image fusion is now introduced. The successful fusion of images acquired from different modalities or instruments is of great importance in many applications, such as medical imaging, microscopic imaging, remote sensing, computer vision, and robotics. With 2D and 3-D imaging and image processing becoming widely used, there is a growing need for new 3-D image fusion algorithms capable of combining 2D & 3-D multimodality or multisource images. Such algorithms can be used in areas such as 2D & 3-D e.g. fusion of images in Target tracking system, Synthetic Aperture Radar (SAR) etc. In case of target tracking system the time is the very important factor. So we take time as a comparison factor to compare different methods which we implement. In order to improve the efficiency of the project, Elapsed time for the fusion to run is being formulated.

[1]  Gonzalo Pajares,et al.  A wavelet-based image fusion tutorial , 2004, Pattern Recognit..

[2]  Dong Sun Park,et al.  Wavelet based approach for fusing computed tomography and magnetic resonance images , 2009, 2009 Chinese Control and Decision Conference.

[3]  D. Abbott,et al.  Information Fusion and Wavelet Based Segment Detection with Applications to the Identification of 3D Target T-ray CT Imaging , 2006, 2006 Joint 31st International Conference on Infrared Millimeter Waves and 14th International Conference on Teraherz Electronics.

[5]  Jinzhu Yang,et al.  PET/CT medical image fusion algorithm based on multiwavelet transform , 2010, 2010 2nd International Conference on Advanced Computer Control.

[6]  Bo Yang,et al.  Image Fusion Using an Improved Max-Lifting Scheme , 2009, 2009 2nd International Congress on Image and Signal Processing.

[7]  D. Abbott,et al.  Terahertz Computed Tomographic Reconstruction and its Wavelet-based Segmentation by Fusion , 2007, 2007 IEEE International Symposium on Industrial Electronics.

[8]  Lei Liu,et al.  A Novel Wavelet Medical Image Fusion Method , 2007, 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07).

[9]  Mircea-Florin Vaida,et al.  Medical image fusion based on discrete wavelet transform using Java technology , 2009, Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces.

[10]  Zhiming Cui,et al.  Medical Image Fusion Based on Wavelet Transform and Independent Component Analysis , 2009, 2009 International Joint Conference on Artificial Intelligence.

[11]  Fang Liu,et al.  Image fusion based on wedgelet and wavelet , 2007, 2007 International Symposium on Intelligent Signal Processing and Communication Systems.

[12]  Sumana Gupta,et al.  Novel Masks for Multimodality Image Fusion using DTCWT , 2005, TENCON 2005 - 2005 IEEE Region 10 Conference.