Abstract
The energy compaction and multiresolution properties of wavelets have made the image fusion successful in combining important features such as edges and textures from source images without introducing any artifacts for context enhancement and situational awareness. The wavelet transform is visualized as a convolution of wavelet filter coefficients with the image under consideration and is computationally intensive. The advent of lifting-based wavelets has reduced the computations but at the cost of visual quality and performance of the fused image. To retain the visual quality and performance of the fused image with reduced computations, a discrete cosine harmonic wavelet (DCHWT)-based image fusion is proposed. The performance of DCHWT is compared with both convolution and lifting-based image fusion approaches. It is found that the performance of DCHWT is similar to convolution-based wavelets and superior/similar to lifting-based wavelets. Also, the computational complexity (in terms of additions and multiplications) of the proposed method scores over convolution-based wavelets and is competitive to lifting-based wavelets.
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Abbreviations
- IR:
-
Infrared
- DWT:
-
Discrete wavelet transform
- WT:
-
Wavelet transform
- MDCT:
-
Multiresolution discrete cosine transform
- HWT:
-
Harmonic wavelet transform
- FT:
-
Fourier transform
- FFT:
-
Fast Fourier transform
- DFT:
-
Discrete Fourier transform
- HWC:
-
Harmonic wavelet coefficient
- DCT:
-
Discrete cosine transform
- IDCT:
-
Inverse DCT
- DCTHWT:
-
Discrete cosine transform harmonic wavelet transform
- DCHWT:
-
Discrete cosine harmonic wavelet transform
- DCHWC:
-
Discrete cosine harmonic wavelet coefficients
References
Blum R., Liu Z.: Multi-sensor Image Fusion and Its Applications. CRC Press, London (2005)
Shah, P., Merchant, S.N., Desai, U.B.: Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition. J. SIViP (2011). doi: 10.1007/s11760-011-0219-7
Shah P., Merchant S.N., Desai U.B.: Fusion of surveillance images in infrared and visible band using curvelet, wavelet and wavelet packet transform. Int. J. Wavelets. Multiresolution Inform. Process. 8(2), 271–292 (2010)
Qu G., Zhang D., Yan P.: Medical image fusion by wavelet transform modulus maxima. Opt. Express 9(4), 184–190 (2001)
Licai, M.Y., Xin, L., Yucui, Y.: Medical image fusion based on wavelet packet transform and self-adaptive operator. In: Proceedings of International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp. 2647–2650 May (2008)
Petrovic, V.: Multisensor Pixel-Level Image Fusion PhD Thesis. Department of Imaging Science and Biomedical Engineering Manchester School of Engineering, United Kingdom (2001)
Hamza A.B., He Y., Krim H., Willsky A.: A multiscale approach to pixel-level image fusion. Integr. Comput. Aided Eng. 12(2), 135–146 (2005)
Li H., Manjunath B.S., Mitra S.K.: Multisensor image fusion using the wavelet transform graph. Models Image Process. 57(3), 235–245 (1995)
Sasikala M., Kumaravel M.: A comparative analysis of feature-based image fusion method. Inf. Tech. J. 6(8), 1224–1230 (2007)
Tao Q., Veldhuis R.: Threshold-optimized decision-level fusion and its application to biometrics. Pattern Recogn. 42, 823–836 (2009)
Carper J.W., Lilles T.M., Kiefer R.W.: The use of intensity-hue saturation transformations for merging SPOT panchromatic and multi-spectra image data. Photogr. Eng. Remote Sens. 56, 459–467 (1990)
Toet A.: Hierarchical image fusion. Mach. Vis. Appl. 3(1), 1–11 (1990)
Haeberli, P.: A Multi-focus Method for Controlling Depth of Field. Grafic Obscura (1994)
Daubechies, I.: Ten Lectures on Wavelets. CBMS-NSF Regional Conference on Series in Applied Mathematics, 61, 2nd edn. SIAM, Philadelphia (1992)
Zhi-guo, J., Dong-bing, H., Jin, C., Xiao-kuan, Z.: A Wavelet based Algorithm for Multi-focus Micro-image Fusion. In: Proceedings of International Conference on Image and Graphics (ICIG), pp. 176–179, Dec (2004)
Ranjith T., Ramesh C.: A lifting wavelet transform based algorithm for multi-sensor image fusion. CRL Tech. J. 3(3), 19–22 (2001)
Hill, P., Canagaraj, N., Bull, D.: Image Fusion using Complex Wavelets. In: Proc. of British Machine Vision Conference (BMVC), pp. 487–496, Sep (2002)
Du Y., Vachon P.W., Vander Sanden J.J.: Satellite image fusion with multi-scale wavelet analysis for marine applications. Can. J. Remote Sens. 29(1), 14–23 (2003)
Wang Z., Ziou D., Armenakis C., Li D., Li Q.: A comparative analysis of image fusion methods. In: IEEE Trans. Geosci. Remote Sens. 43(6), 1392–1402 (2005)
Zheng S., Shi W.-Z., Liu J., Zhu G.-X., Tian J.-W.: Multisource image fusion method using support value transform. In: IEEE Trans. Image Process. 16(7), 1831–1839 (2007)
Li S., Kwok J.T., Wang Y.: Multifocus image fusion using artificial neural networks. Pattern Recogn. Lett. 23, 985–997 (2002)
Hao Y., Sun Z., Tan T.: Comparative studies on multispectral palm image fusion for biometrics. ACCV, Part II, LNCS 4844, 12–21 (2007)
Arivazhagan S., Ganesan L., Subash Kumar T.G.: A modified statistical approach for image fusion using wavelet transform. J. SIViP 3(2), 137–144 (2009)
Shah, P., Srikanth, T.V., Merchant, S.N., Desai, U.B.: A novel multifocus image fusion scheme based on pixel significance using wavelet transform. In: Proceedings of Image, Video, and Multidimensional Signal Process (IVMSP), pp. 54–59, Aug (2011)
Rahman S.M.M., Ahmad M.O., Swamy M.N.S.: Contrast-based fusion of noisy images using discrete wavelet transform. IET Image Process. 4(5), 374–384 (2010). doi:10.1049/iet-ipr.2009.0163
Naidu V.P.S.: Discrete cosine transform-based image fusion. Def. Sci. J. 60(1), 48–54 (2010)
Newland D.E.: Harmonic wavelet analysis. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 443(1917), 203–225 (1993)
Newland, D.E.: Time-frequency and time-scale signal analysis by harmonic Wavelets. In: Proceedings of European Conference on Signal Analysis and Prediction, pp. 53–59, Prague, Jun (1997)
Newland D.E.: Random Vibrations, Spectral and Wavelets Analysis. 3rd edn. Longman, Singapore (1993)
Narasimhan, S.V., Harish, M.: Spectral estimation based on subband decomposition by harmonic wavelet transform and modified group delay. In: Proceedings of International Conference on Signal Processing and Communications (SPCOM), pp. 349–353, Bangalore, Dec (2004)
Tezcan J.: Evolutionary power spectrum estimation using harmonic wavelets. J. Seism. Des. Anal. Struct. Chap. 6, 37–41 (1999)
Gurlzow T., Ludwig T., Heute U.: Spectral-subtraction speech enhancement in multirate systems with and without non-uniform and adaptive bandwidths. Signal Process. 83(8), 1613–1631 (2003)
Shreyamsha Kumar, B.K., Prabhakar, B., Suryanarayana, K., Thilagavathi, V.: Harmonic wavelet based ISAR imaging for target identification. In: Proceedings of International Radar Symposium India (IRSI), pp. 343–348, Bangalore, Dec (2005)
Shreyamsha Kumar B.K., Prabhakar B., Suryanarayana K., Thilagavathi V., Rajagopal R.: Target identification using harmonic wavelet based ISAR imaging. J. Appl. Signal Process. 2006, 1–13 (2006)
Cattani C.: Harmonic wavelets towards the solution of nonlinear PDE. Comput. Math. Appl. 50(8), 1191–1210 (2005)
Shreyamsha Kumar, B.K.: Group Delay Functions for Time Frequency Representation and Spectral Estimation, M.Tech Thesis, Department of Electronics & Communication Engineering, National Institute of Technology Karnataka, Surathkal (2004)
Narasimhan, S.V., Shreyamsha Kumar, B.K.: Harmonic wavelet transform signal decomposition and modified group delay for improved Wigner–Ville distribution. In: Proceedings of International Conference on Signal Processing and Communications (SPCOM), pp. 354–358, Bangalore, Dec (2004)
Narasimhan, S.V., Harish, M.: Discrete cosine harmonic wavelet transform and its application to subband spectral estimation using modified group delay. In: Pai, B.R. Dr. (eds.) Proceedings of Conference in Honor of Dr. B. R. Pai. National Aerospace Laboratories, Bangalore (2004)
Narasimhan S.V., Harish M., Haripriya A.R., Basumallick N.: Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay. J. SIViP 3(1), 85–99 (2009). doi:10.1007/s11760-008-0062-7
Shreyamsha Kumar B.K.: Image Denoising using Discrete Cosine Harmonic Wavelets. Technical Report, Sensor Signal Process. Group, Central Research Laboratory, Bangalore, Jul (2010)
Shivamurti, M., Narasimhan, S.V.: Analytic discrete cosine harmonic wavelet transform (ADCHWT) and its application to signal/image denoising. In: Proceedings of International Conference on Signal Processing and Communications (SPCOM), pp. 1–5, Bangalore, Jul (2010)
Narasimhan, S.V., Veena, S.: A new delayless subband adaptive filter based on discrete cosine harmonic wavelet transform (CHWT). In: Proceedings of International Conference on Signal Processing and Communications (SPCOM), pp. 1–5, Bangalore, Jul (2010)
Narasimhan S.V., Haripriya A.R., Shreyamsha Kumar B.K.: Improved Wigner–Ville distribution performance based on DCT/DFT harmonic wavelet transform and modified magnitude group delay. Signal Process. 88(1), 1–18 (2008)
Raghuveer M.R., Bopardikar A.S.: Wavelet Transforms Introduction to Theory and Applications. Pearson Education Pte. Ltd., Delhi (2004)
Samar, V.J., Begleiter, H., Chapa, J.O., Raghuveer, M.R., Orlando, M., Chorlian, D.: Matched meyer neural wavelets for clinical and experimental analysis of auditory and visual evoked potentials. In: Proceedings of European Signal Processing Conference (EUSIPCO), pp. 387–390, Sep (1996)
Cattani C.: Shannon wavelets theory. J. Math. Probl. Eng. 2009, 1–24 (2009). doi:10.1155/2008/164808
Cattani C.: Harmonic wavelet solutions of the Schrodinger equation. Int. J. Fluid Mech. Res. 30(5), 1–10 (2003)
Cattani C., Kudreyko A.: On the discrete harmonic wavelet transform. J. Math. Probl. Eng. 2008, 1–7 (2008). doi:10.1155/2008/687318
Narasimhan S.V., Veena S.: Signal Processing: Principles and Implementation, Revised Edition. Narosa Publishing House Pvt. Ltd, India (2008)
Britanak V., Yip P.C., Rao K.R.: Discrete Cosine and Sine Transforms. 1st edn. Academic Press, Great Britain (2007)
Petrovic, V., Xydeas, C.: Objective image fusion performance characterization. In: Proceedings of International Conference on Computer Vision (ICCV), vol. 2, pp. 1866–1871, Oct (2005)
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Shreyamsha Kumar, B.K. Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform. SIViP 7, 1125–1143 (2013). https://doi.org/10.1007/s11760-012-0361-x
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DOI: https://doi.org/10.1007/s11760-012-0361-x