Abstract
Pixel level image fusion algorithms using Bayes entropy measure of focus (BH) and spatial frequency (SF) are presented and their performances are compared. Both algorithms are performed almost alike. In fact Bayes entropy measure of focus based image fusion algorithm shows slightly better performance. In terms of computational complexity, SF is better than the BH and it can be used for real time applications. The performances of these algorithms are superior to with the well known image fusion technique based on wavelets.
References
G. Pajares, J.M. de la Cruz, A wavelet-based image fusion tutorial. Pattern Recogn. 37, 1855–1872 (2007)
P.K. Varsheny, Multisensor data fusion. Electron. Commun. Eng. J. 9(12), 245–253 (1997)
P.J. Burt, R.J. Lolczynski, Enhanced image capture through fusion. In Proc The 4th Int Conf on Computer Vision (Berlin, Germany, 1993), pp. 173–182.
S.G. Mallet, A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intel. 11(7), 674–693 (1989)
H. Wang, J. Peng, W. Wu, Fusion algorithm for multisensor image based on discrete multiwavelet transform. IEE Proc. Vis. Image Signal Process 149(5), 283–289 (2002)
H. Li, B.S. Manjunath, S.K. Mitra, Multisensor image fusion using wavelet transform. Graph. Models Image Process 57(3), 235–245 (1995)
B. Ajazzi, L. Alparone, S. Baronti, R. Carla, Assessment pyramid-based multisensor image data fusion. Proc. SPIE 3500, 237–248 (1998)
A. Akerman, Pyramid techniques for multisensory fusion. Proc. SPIE 2828, 124–131 (1992)
A. Toet, L.J. Van Ruyven, J.M. Valeton, Merging thermal and visual images by a contrast pyramid. Opt. Eng. 28(7), 789–792 (1989)
S. Li, J.T. Kwok, Y. Wang, Combination of images with diverse focuses using the spatial frequency. Inf. Fusion 2(3), 167–176 (2001)
V.P.S. Naidu, J.R. Raol, Fusion of out of focus images using principal component analysis and spatial frequency. J. Aerosp. Sci. Technol. 60(3), 216–225 (2008)
R.S. Blum, Robust image fusion using a statistical signal processing approach. Image Fusion 6, 119–128 (2005)
A. Nejatali, L.R. Ciric, Novel image fusion methodology using fuzzy set theory. Opt. Eng. 37(2), 485–491 (1998)
M. Kristan, J. Pers, M. Perse, S. Kovacic, A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform. Pattern Recognit. Lett. 27(13), 1419–1580 (2006)
A.M. Eskicioglu, P.S. Fisher, Image quantity measures and their performance. IEEE Trans. Commun. 43(12), 2959–2965 (1995)
P.A. Devijver, On a new class of bounds on Bayes risk in multihypothesis pattern recognition. IEEE Trans. Comput. C 23, 70–80 (1974)
V.P.S. Naidu, G. Girija, J.R. Raol, Evaluation of data association and fusion algorithms for tracking in the presence of measurement loss. AIAA Conference on Navigation, Guidance and Control, Austin, USA, 11–14, August 2003.
G.R. Arce, Nonlinear signal processing—a statistical approach (Wiley-Interscience Inc., Publication, USA, 2005)
R.S. Blum, Z. Liu, Multi-sensor image fusion and its applications (CRC Press, Taylor & Francis Group, Boca Raton, 2006)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Naidu, V.P.S. Multi focus image fusion using the measure of focus. J Opt 41, 117–125 (2012). https://doi.org/10.1007/s12596-012-0071-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12596-012-0071-3