Multiscale image fusion using an adaptive similarity-based sensor weighting scheme and human visual system-inspired contrast measure

Journal of Electronic Imaging

Volumn 21, Issue 2, 2012, Pages

  Nercessian, Shahan C ^a   Panetta, Karen A ^a   Agaian, Sos S ^b  

^a Tufts University   (United States)

^b The University of Texas at San Antonio   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS;

APPROXIMATION COEFFICIENTS; DETAIL COEFFICIENTS; MACHINE PERCEPTION; MULTISCALE CONTRAST; MULTISCALE IMAGE FUSIONS; PSYCHOPHYSICAL LAWS; SALIENT FEATURES; WEIGHTING SCHEME;

IMAGE FUSION;

EID: 84892730400     PISSN: 10179909     EISSN: 1560229X     Source Type: Journal    
DOI: 10.1117/1.JEI.21.2.021112     Document Type: Conference Paper

References (27)

1. M. Kumar and S. Dass, "A total variation-based algorithm for pixellevel image fusion," IEEE Trans. Image Process. 18(9), 2137-2143 (2009).
2. S. Nercessian, K. Panetta, and S. Agaian, "Multiresolution decomposition schemes using the parameterized logarithmic image processing model with application to image fusion," EURASIP Journal on Advances in Signal Processing 2011, Article ID 515084, 1-17 (2011), [DOI: 10.1155/2011/515084].
3. S. Agaian, K. Panetta, and S. Nercessian, "Image fusion using a parameterized logarithmic image processing framework," Image Fusion O. Ukimura, Ed., INTEC (2011).
4. S. Daneshvar and H. Ghassemian, "MRI and PET image fusion by combining IHS and retina-inspired models," Inform. Fusion 11(2), 114-123 (2010).
5. Z. Zhang and R. Blum, "A categorization of multiscale- decompositionbased image fusion schemes with a performance study for a digital camera application," Proc. IEEE 87(8), 1315-1326 (1999).
6. Z. X. Qiong, G. Z. Sheng, and Y. H. Zhao, "Dynamic infrared and visible image sequence fusion based on DT-CWT using GGD," International Conference on Computer Science and Information Technology, pp. 875-878, IEEE (2008).
7. P. S. Chavez and A. Y. Kwarteng, "Extracting spectral contrast in Landsat Thematic Mapper image data using selective component analysis," Photogramm. Eng. Rem. Sens. 55(3), 339-348 (1989).
8. G. Piella, "A general framework for multiresolution image fusion: from pixels to regions," Inform. Fusion 4(4), 259-280 (2003).
9. F. Yang, Y. Chang, and S. Wan, "Gradient-threshold edge detection based on the human visual system," Opt. Eng. 44(2), 020205 (Feb. 2005).
10. K. Panetta, E. Wharton, and S. Agaian, "Human visual system based image enhancement and logarithmic contrast measure," IEEE Trans. Syst. Man Cybern. B Cybern. 38(1), 174-188 (2008).
11. T. Pu and Q. Ni, "Contrast-based image fusion using the discrete wavelet transform," Opt. Eng. 39(8), 2075-2082 (2000).
12. A. Toet, L. Van Ruyven, and J. Valeton, "Merging thermal and visual images by a contrast pyramid," Opt. Eng. 28(7), 789-792 (1989).
13. L. Krueger, "Reconciling Fechner and Stevens: toward a unified psychophysical law," Behav. Brain Sci. 12, 251-267 (1989).
14. S. Dippel et al., "Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform," IEEE Trans. Med. Imag. 21(4), 343-353 (Apr. 2002).
15. G. Piella and H. Heijmans, "A new quality metric for image fusion," in International Conference on Image Processing 2, 173-176, IEEE (Sep. 2003).
16. E. H. Adelson et al., "Pyramid methods in image processing," RCA Engineer 29(6), 33-41 (1984).
17. S. Agaian, "Visual Morphology," Proc. SPIE 3646, 139-150 (1999).
18. J. Tang, E. Peli, and S. Acton, "Image enhancement using a contrast measure in the compressed domain," IEEE Signal Process. Lett. 10(10), 289-292 (2003).
19. Z. Wang and A. C. Bovik, "Mean squared error: love it or leave it? A new look at signal fidelity measures," IEEE Signal Process. Mag. 26(1), 98-117 (2009).
20. Z. Wang et al. "Image quality assessment: from error visibility to structural similarity," IEEE Trans. Image Process. 13(4), 1-12 (2004).
21. G. H. Chen, C. L. Yang, and S. L. Xie, "Gradient-based structural similarity for image quality assessment," in IEEE International Conference on Image Processing, 2929-2932, IEEE (2006).
22. W. Wang, P. Shui, and X. Feng, "Variational Models for Fusion and Denoising of Multifocus Images" IEEE Signal Process. Lett. 15, 65-68 (2008).
23. S. Nercessian, K. Panetta, and S. Agaian, "Human visual system-based image fusion for surveillance applications," in IEEE International Conference on Systems, Man, and Cybernetics 2687-2691, IEEE (2011).
24. C. Li and A. C. Bovik, "Content-partitioned structural similarity index for image quality assessment," Signal Process. Image Comm. 25(7), 517-526 (Aug. 2010).
25. C. S. Xydeas and V. Petrović, "Objective image fusion performance measure," Electron. Lett. 36(4), 308-309 (2000).
26. G. Qu, D. Zhang, and P. Yan, "Information measure for performance of image fusion," Electron. Lett. 38(7), 313-315 (2002).
27. Methods for Subjective Determination of Transmission Quality, ITU-T Recommendation P. 800 (1996).