Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

Full Text (PDF, 687KB), PP.37-45

Views: 0 Downloads: 0


Sonam 1,* Manoj Kumar 1

1. Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2017.01.04

Received: 1 Apr. 2016 / Revised: 22 Jun. 2016 / Accepted: 15 Aug. 2016 / Published: 8 Jan. 2017

Index Terms

Image Fusion, Discrete Wavelet Transform, Cross Bilateral Filter, Standard Deviation, Correlation Coefficients


The main objective of image fusion is to obtain an enhanced image with more relevant information by integrating complimentary information from two source images. In this paper, a novel image fusion algorithm based on discrete wavelet transform (DWT) and cross bilateral filter (CBF) is proposed. In the proposed framework, source images are decomposed into low and high frequency subbands using DWT. The low frequency subbands of the transformed images are combined using pixel averaging method. Meanwhile, the high frequency subbands of the transformed images are fused with weighted average fusion rule where, the weights are computed using CBF on both the images. Finally, to reconstruct the fused image inverse DWT is performed over the fused coefficients. The proposed method has been extensively tested on several pairs of multi-focus and multisensor images. To compare the results of proposed method with different existing methods, a variety of image fusion quality metrics are employed for the qualitative measurement. The analysis of comparison results demonstrates that the proposed method exhibits better results than many other fusion methods, qualitatively as well as quantitatively.

Cite This Paper

Sonam, Manoj Kumar,"Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.1, pp.37-45, 2017. DOI:10.5815/ijisa.2017.01.04


[1]Blum R S, Liu Z. Multi-sensor Image Fusion and Its Applications. CRC Press, Taylor & Francis Group, 2005.
[2]Hill D, Edwards P, Hawkes D. Review of pixel-level image fusion. Fusing medical images [J]. Image Processing, 1994, 6(2): 22-24.
[3]Qu G H, Zhang D L, Yan P E. Medical image fusion by wavelet transform modulus maxima [J]. Opt Express, 2001, 9(4): 184-190.
[4]Smith M I, Ball A N, Hooper D. Real-time image fusion: A vision aid for helicopter pilotage [J]. Proc SPIE, 2002, 4713: 83-94.
[5]Slamani M A, Ramac L C, Uner M K, Varshney P K, Weiner D D, Alford M G, Ferris D D, Vannicola V C. Enhancement and fusion of data for concealed weapons detection [J]. Proc SPIE, 1997, 3068: 8-19.
[6]Daniel M M, Willsky A S. A multiresolution methodology for signal-level fusion and data assimilation with applications to remote sensing [J]. Proc IEEE, 1997, 85(1): 164-180.
[7]Mitianoudis N, Stathaki T. Pixel-based and region-based image fusion schemes using ICA bases. Inf. Fusion, 2007, 8 (2): 131-42.
[8]Li H, Manjunath B S, Mitra S K. Multisensor image fusion using the wavelet transform [J]. Graph Models Image Process, 1995, 57(3): 235-245.
[9]Petrovic V. Multisesor pixel-level image fusion. PhD Thesis, Department of Imaging Science and Biomedical Engineering, Manchester School of Engineering, United Kingdom, 2001.
[10]Shreyamsha Kumar B K, Swamy M N S, Omair Ahmad M. Multiresolution DCT decomposition for multifocus image fusion. Proceedings of the Canadian Conference on Electrical and Computer Engineering (CCECE), Regina, Canada, 2013, 1-4.
[11]Goshtasby A A, Nikolov S. Image fusion: advances in the state of the art. Inf. Fusion, 2007, 8(2): 114-118.
[12]Huafeng Li, Shanbi W, Chai Yi. Multifocus image fusion scheme based on feature contrast in the lifting stationary wavelet domain. EURASIP Journal on Advances in Signal Processing. 2012, 39: 1-16.
[13]Selesnick I W, Baraniuk R G, Kingsbury N C. The dual-tree complex wavelet transform. IEEE Signal Process, Mag, 2005, 22 (6): 123-151.
[14]Ali F E, El-Dokany I M, Saad A A, Abd El-Samie F E. A curvelet transform approach for the fusion of MR and CT images. Journal of Modern Optics, Taylor & Francis. 2010, 57 (4): 273-286.
[15]Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. on Image Processing, 2005, 14(12): 2091–2106.
[16]Zhang Q, Guo B L. Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing, 2009, 89(7): 1334-1346.
[17]Naidu V P S. Image fusion technique using multiresolution singular value decomposition. Defence Sci. J., 2011, 61 (5): 479-484.
[18]Shutao Li, Xudong Kang, Jianwen Hu, Bin Yang. Image matting for fusion of multi-focus images in dynamic scenes. Inf. Fusion, 2013, 14: 147-162.
[19]Naidu V P S. Discrete cosine transform-based image fusion, Defence Sci. J., 2010, 60 (1): 48-54.
[20]Sonam Gautam, Manoj Kumar. An Effective Image Fusion Technique based on Multiresolution Singular Value Decomposition. INFOCOMP Journal of Computer Science, 2015, 14 (2): 31-43.
[21]Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision, 1998: 839–846.
[22]Manoj Diwakar, Sonam, Manoj Kumar. CT image denoising based on complex wavelet transform using local adaptive thresholding and Bilateral filtering. ACM, Proceedings of the Third International Symposium on Women in Computing and Informatics (WCI), 2015, 297-302.
[23]Jianwen Hu, Shutao Li. The multiscale directional bilateral filter and its application to multisensor image fusion. Inf. Fusion, 2012, 13(3): 196-206.
[24]Shreyamsha Kumar B K. Image fusion based on pixel significance using cross bilateral filter. SIVIP, 2013.
[25]Petschnigg G, Agrawala M, Hoppe H, Szeliski R, Cohen M,Toyama K. Digital photography with flash and no-flash image pairs. ACM Trans. Gr. 2004, 23(3): 664–672.
[26]Naidu V P S, Raol J R. Pixel-level image fusion using wavelets and principle component analysis. Defence Sci. J., 2008, 58 (3): 338-352.
[27]Li S, Kwok J T, Wang Y. Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inf. Fusion, 2002, 3(1): 17–23.
[28]Wang Z, Ziou D, Armenakis C, Li D, Li Q. A Comparative Analysis of Image Fusion Methods. IEEE Trans. on Geoscience and Remote Sensing, 2005, 43 (6): 1391 - 1402.
[29]Petrovic V, Xydeas C. Objective image fusion performance characterization. In: Proceedings of the International Conference on Computer Vision (ICCV), 2005, 2: 1866–1871.
[30]Eleyan Alaa. Enhanced face recognition using data fusion. I. J. Intelligent systems and applications, 2013, 01: 98-103.
[31]Kaur S, Dadhwal H S. Biorthogonal wavelet transform using bilateral filter and adaptive histogram equalization. I. J. Intelligent systems and applications, 2015, 03: 37-43.