Library Subscription: Guest
Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections
International Journal for Multiscale Computational Engineering
IF: 1.016 5-Year IF: 1.194 SJR: 0.452 SNIP: 0.68 CiteScore™: 1.18

ISSN Print: 1543-1649
ISSN Online: 1940-4352

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.v8.i6.60
pages 631-640

Energy-Based Fusion Scheme for Surveillance and Navigation

S. Senthil Kumar
Research Scholar, Anna University Chennai, Department of Electronics and Communication Engineering, Anna University, Chennai 600025, India
S. Muttan
Professor, Anna University Chennai, Department of Electronics and Communication Engineering, Anna University, Chennai 600025, India
K. Mahesh Bharath
St. Joseph’s College of Engineering Chennai, Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering, Chennai 600119, India

ABSTRACT

Image fusion is used to integrate multiple images into a composite image which contains complementary information from each of the source images. In defense applications, fusion is widely employed to obtain images pertaining to the object under surveillance and also for mapping terrain for navigation purposes. Although there are a many fusion algorithms reported in the literature, the need is for a computationally efficient fusion rule that can be implemented easily in hardware. Driven by this motivation we have formulated a discrete wavelet transform{based fusion technique that uses the energy of the wavelet coefficients to determine the fusion weights for the approximate image and choose maximum intensity rule for the detail image. Surveillance imaging generally uses two imaging sources, one an infrared camera and the other a conventional digital camera, and the images are usually captured under low lighting and night-time conditions. We used the structural similarity index, mutual information, and standard deviation as metrics to evaluate the performance of our fusion scheme with existing algorithms. Our experiments have shown that the algorithm developed produces good results under the constraints imposed by this application.

REFERENCES

  1. Burt, P. J. and Adelson, E. H., The Laplacian pyramid as a compact image code. DOI: 10.1109/TCOM.1983.1095851

  2. Burt, P. J., The pyramid as a structure for efficient computation. DOI: 10.1007/978-3-642-51590-3_2

  3. Das, S., Zhang, Y.-L., and Krebs, W. K., Color night vision for navigation and surveillance. DOI: 10.3141/1708-05

  4. Canga, E. F., Nikolov, S. G., Canagarajah, C. N., Bull, D. R., Dixon, T. D., Noyes, J. M., and Troscianko, T., Characterization of image fusion quality metrics for surveillance applications over band-limited channels. DOI: 10.1109/ICIF.2005.1591894

  5. Chen, Y. and Blum, R. S., Experimental tests of image fusion for night vision. DOI: 10.1109/ICIF.2005.1591895

  6. Image Fusion, <a href="http://www.imagefusion.org">www.imagefusion.org</a>.

  7. Kim, M. G., Dinstein, I., and Shaw, L., A prototype filter design approach to pyramid generation. DOI: 10.1109/34.250842

  8. Maruthi, R. and Suresh, R. M., Metrics for measuring the quality of fused images. DOI: 10.1109/ICCIMA.2007.44

  9. Qu, G., Zhang, D., and Yan, P., Information measure for performance of image fusion. DOI: 10.1049/el:20020212

  10. Toet, A., Image fusion by a ratio of low pass pyramid. DOI: 10.1016/0167-8655(89)90003-2

  11. Toet, A., van Ruvven, L. J., and Valeton, J. M., Merging thermal and visual images by a contrast pyramid. DOI: 10.1117/12.7977034

  12. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P., Image quality assessment: From error visibility to structural similarity. DOI: 10.1109/TIP.2003.819861

  13. Zhang, Z. and Blum, R. S., A categorization of multiscaledecomposition-based image fusion schemes with a performance study for a digital camera application. DOI: 10.1109/5.775414

  14. Zheng, Y., Hou, X., Bian, T., and Oin, Z., Effective image fusion rules of multiscale image decomposition. DOI: 10.1109/ISPA.2007.4383720


Articles with similar content:

Relaxations of Attainable Sets and Their Generalized Representation in the Class of Two-Valued Finitely Additive Measures
Journal of Automation and Information Sciences, Vol.29, 1997, issue 1
Alexander G. Chentsov, O. A. Cherepanova
A CALIBRATION METHOD OF A TWO-CAMERA SYSTEM IN A FACE-TO-FACE CONFIGURATION DESIGNED FOR FAST-FLOW STUDY
Journal of Flow Visualization and Image Processing, Vol.7, 2000, issue 2
L. Riou, Jacques Jay, G. Jacquet, R. Fouquet, J. Fayolle
UNCERTAINTY QUANTIFICATION TOWARDS FILTERING OPTIMIZATION IN SCENE MATCHING AIDED NAVIGATION SYSTEMS
International Journal for Uncertainty Quantification, Vol.6, 2016, issue 2
Xiaojun Duan, Shengdi Zhang, Lijun Peng
HIGH-SPEED COLOR INTERFEROMETRY
Journal of Flow Visualization and Image Processing, Vol.11, 2004, issue 4
Jean-Michel Desse
NEAR-WALL STRUCTURE IN A FULLY DEVELOPED TURBULENT PIPE FLOW
TSFP DIGITAL LIBRARY ONLINE, Vol.1, 1999, issue
Guixiang Cui, Zhaoshun Zhang, Michel Ayrault, Serge Simoens, Guoqing Wang