Multi-focus Image Fusion Using Sparse Representation and Modified Difference
Conference paper
First Online:
Abstract
Multi-focus fusion technique is used to combine images obtained from single or different cameras with different focal distance, etc. In the proposed method, the non-subsampled shearlet transform (NSST) is employed to decompose the input image data into the low-frequency and high-frequency bands. These low-frequency and high-frequency bands are combined using sparse representation (SR) and modified difference based fusion rules, respectively. Then, inverse NSST is employed to get the fused image. Both qualitative and quantitative results confirm that the proposed approach yields a better performance as compared to state-of-the-art fusion schemes.
Keywords
Image fusion K-SVD Cosine bases Multi-focus Sparse representation Dictionary learningReferences
- 1.Cao, L., Jin, L., Tao, H., Li, G., Zhuang, Z., Zhang, Y.: Multi-focus image fusion based on spatial frequency in discrete cosine transform domain. IEEE Signal Process. Lett. 22(2), 220–224 (2015)CrossRefGoogle Scholar
- 2.Easley, G., Labate, D., Lim, W.Q.: Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harmon. Anal. 25(1), 25–46 (2008)MathSciNetCrossRefGoogle Scholar
- 3.Guo, K., Labate, D.: Optimally sparse multidimensional representation using shearlets. SIAM J. Math. Anal. 39(1), 298–318 (2007)MathSciNetCrossRefGoogle Scholar
- 4.Guorong, G., Luping, X., Dongzhu, F.: Multi-focus image fusion based on non-subsampled shearlet transform. IET Image Process. 7(6), 633–639 (2013)CrossRefGoogle Scholar
- 5.Li, H., Manjunath, B., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graph. Models Image Process. 57(3), 235–245 (1995)CrossRefGoogle Scholar
- 6.Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864–2875 (2013)CrossRefGoogle Scholar
- 7.Liang, J., He, Y., Liu, D., Zeng, X.: Image fusion using higher order singular value decomposition. IEEE Trans. Image Process. 21(5), 2898–2909 (2012)MathSciNetCrossRefGoogle Scholar
- 8.Liu, Y., Liu, S., Wang, Z.: A general framework for image fusion based on multi-scale transform and sparse representation. Inf. Fusion 24, 147–164 (2015)CrossRefGoogle Scholar
- 9.Liu, Y., Liu, S., Wang, Z.: Multi-focus image fusion with dense SIFT. Inf. Fusion 23, 139–155 (2015)CrossRefGoogle Scholar
- 10.Malvar, H.S.: Signal Processing with Lapped Transforms. Artech House, Norwood (1992)zbMATHGoogle Scholar
- 11.Meyer, F.G.: Image compression with adaptive local cosines: a comparative study. IEEE Trans. Image Process. 11(6), 616–629 (2002)CrossRefGoogle Scholar
- 12.Petrovic, V.S., Xydeas, C.S.: Gradient-based multiresolution image fusion. IEEE Trans. Image Process. 13(2), 228–237 (2004) CrossRefGoogle Scholar
- 13.Vishwakarma, A., Bhuyan, M.K.: Image fusion using adjustable non-subsampled shearlet transform. IEEE Trans. Instrum. Measur. 68(9), 1–12 (2018)Google Scholar
- 14.Xydeas, C., Petrovic, V.: Objective image fusion performance measure. Electron. Lett. 36(4), 308–309 (2000)CrossRefGoogle Scholar
- 15.Yang, C., Zhang, J.Q., Wang, X.R., Liu, X.: A novel similarity based quality metric for image fusion. Inf. Fusion 9(2), 156–160 (2008)CrossRefGoogle Scholar
- 16.Yang, Y., Tong, S., Huang, S., Lin, P.: Multifocus image fusion based on nsct and focused area detection. IEEE Sens. J. 15(5), 2824–2838 (2015)Google Scholar
- 17.Yin, H., Li, S., Fang, L.: Simultaneous image fusion and super-resolution using sparse representation. Inf. Fusion 14(3), 229–240 (2013)CrossRefGoogle Scholar
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