In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 Shah, P., Merchant, S.N., Desai, U.B. (2013). Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition. Signal Image and Video Processing, 7(1), 95-109.
 Chai, Y., Li, H.F., Li, Z.F. (2011). Multifocus image fusion scheme using focused region detection and multiresolution. Optics Communications, 284 (19), 4376-4389.
 Zhang, B.H., Zhang, C.T., Liu, Y.Y., Wu, J.S., He, L. (2014). Multi-focus image fusion algorithm based on compound PCNN in Surfacelet domain. Optik, 125(1), 296-300.
 Goshtasby, A.A., Nikolov, S.G. (2007). Image fusion: Advances in the state of the art. Information Fusion, 8(2), 114-118.
 Smith, M.I., Heather, J.P. (2005). Review of image fusion technology in 2005. In Thermosense XXVII. SPIE, Vol. 5782, 29-45.
 Yang, B., Jing, Z.L., Zhao, H.T. (2010). Review of pixel-level image fusion. Journal of Shanghai Jiaotong University (Science), 15 (1), 6-12.
 Li, H., Manjunath, B.S., Mitra, S.K. (1995). Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57 (3), 235- 245.
 Zheng, Y.F., Essock, E.A., Hansen, B.C., Haun, A.M. (2007). A new metric based on extended spatial frequency and its application to DWT based fusion algorithms. Information Fusion, 8 (2), 177-192.
 Yang, Y., Park, D.S., Huang, S.Y., Yang, J.C. (2010). Fusion of CT and MR images using an improved wavelet based method. Journal of X-Ray Science and Technology, 18 (2), 157-170.
 Chen, Y.Q., Chen, L.Q., Gu, H.J., Wang, K. (2010). Technology for multi-focus image fusion based on wavelet transform. In Third International Workshop on Advanced Computational Intelligence, 25-27 August 2010. IEEE, 405-408.
 Burt, P.J., Kolczynski, R.J. (1993). Enhanced image capture through fusion. In Fourth International Conference on Computer Vision, 1-14 May 1993. IEEE, 173-182.
 Deshmukh, M., Bhosle, U. (2011). A Survey of image registration. International Journal of Image Processing, 5 (3), 245-269.
 Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H. (2011). A non-reference image fusion metric based on mutual information of image features. Computers and Electrical Engineering, 37 (5), 744-756.