Abstract
The image fusion technique aims to generate a comprehensive image that can combine complementary information from different source images. Traditional signal processing-based image fusion methods are well-studied in recent years, and researchers try to explore new ideas for this task. Especially, the machine learning method likes support vector machine (SVM) has an apparent advantage in many situations. In this work, a sliding window technique is first used to extract the key features of source images based on several effective evaluation metrics which can reflect the detailed information. Second, the extracted image features are employed to distinguish the focused areas and unfocused areas of source images by a trained SVM model, so the decision results for each source image will be obtained. Third, consistency verification (CV) is utilized to assimilate a single singular point of a specific region in the decision results in order to correct possible errors. At last, a new weighted fusion method for the pixel is designed based on principal component analysis (PCA) to deal with the disputed areas which come from the same position of decision maps of different source images. Experimental results reveal the proposed method is effective and can achieve better performance than comparative methods.
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References
Zhaobin, W., Yide, M., Jason, G.: Multi-focus image fusion using PCNN. Pattern Recogn. 43(6), 2003–2016 (2010)
Jia, Y., Rong, C., Wang, Y., Zhu, Y., Yu, Y.: A multi-focus image fusion algorithm using modified adaptive PCNN model. In: 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 612–617, October 2016
Jin, X., Chen, G., Hou, J.: Multimodal sensor medical image fusion based on nonsub sampled shearlet transform and S-PCNNs in HSV space. Sig. Process. 153, 379–395 (2018)
MarÃa, G.A., José, L.S.: Fusion of multispectral and panchromatic images using improved HIS and PCA mergers based on wavelet decomposition. IEEE Trans. Geosci. Remote Sens. 42(6), 1291–1299 (2004)
Kinoshita, Y., Kiya, H.: Scene segmentation-based luminance adjustment for multi-exposure image fusion. IEEE Trans. Image Process. (2019, In Press)
Yu, L., Chen, X., Peng, H., Wang, Z.: Multi-focus image fusion with a deep convolutional neural network. Inf. Fus. 26, 191–207 (2017)
Liu, S., Chen, J., Rahardja, S.: A new multi-focus image fusion algorithm and its efficient implementation. IEEE Trans. Circ. Syst. Video Technol. (2019, In Press)
Ebenezer, D.: Optimum wavelet-based homomorphic medical image fusion using hybrid genetic-grey wolf optimization algorithm. IEEE Sens. J. 18(16), 6804–6811 (2018)
Xiaorong, X., Fang, X., Hongfu, W., et al.: A parallel fusion algorithm of remote sensing images based on wavelet transform. In: Proceedings 10th IEEE International Conference of High Performance Computing and Communications (HPCC), pp. 13–15, November 2013
Naidu, V.P.S.: Image fusion technique using multi-resolution singular value decomposition. Defence Sci. J. 61(5), 479–484 (2011)
Kumar, S.: Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform signal. Sig. Image Video Process. 7(6), 1125–1143 (2013)
Zhou, Z.Q., Sun, L.: Multi-scale weighted gradient-based fusion for multi-focus images. Inf. Fus. 20, 60–72 (2014)
Atencio-Ortiz, P., Sanchez-Torres, G., Branch-Bedoya, J.W.: Evaluating supervised learning approaches for spatial-domain multi-focus image fusion. Dyna 84(202), 137–146 (2017)
Yu, B.T.: Jia B, Lu D, Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion. Neurocomputing 182, 1–9 (2016)
Guo, X.P., Nie, R.C., Cao, J.D.: Fully convolutional network-based multifocus image fusion. Neural Comput. 30(7), 1775–1800 (2018)
Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43(12), 2959–2965 (1995)
Cherkassky, V.: The nature of statistical learning theory. IEEE Trans. Neural Netw. 8(6), 1564 (1997)
Chen, S., Cowan, C., Grant, P.: Orthogonal least-squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2(2), 302–309 (1991)
Oliver, R.: Pixel-level image fusion and the image fusion toolbox, 30 December 1999. http://www.metapix/toolbox.htm
Jiang, Q., Jin, X., Lee, S.J., et al.: A novel multi-focus image method based on stationary wavelet transform and local features of fuzzy sets. IEEE Access. 5, 20286–20302 (2017)
Liu, Y., Chen, X., Ward, R.K., et al.: Image fusion with convolutional sparse representation. IEEE Sig. Process. Letters 23(12), 1882–1886 (2016)
Paul, S., Sevcenco, L.S., Agathoklis, P.: Multi-exposure and multi-focus image fusion in gradient domain. J. Circ. Syst. Comput. 25(10), 1650123 (2016)
Petrovic, V., Xydeas, C.: Objective image fusion performance characterization. In: 10th IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 1866–1871 (2005)
Acknowledgment
This study is supported by the National Natural Science Foundation of China (No. 61762089, 61863036), China Postdoctoral Science Foundation (2019M653507), and Doctoral Candidate Academic Award of Yunnan Province in China.
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Yang, Y., Jiang, Q., Yao, S., Xue, G., Wu, L., Jin, X. (2020). A Spatial Fusion Scheme of Multi-focus Image Combining SVM-Based Classification and PCA-Based Weight. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_42
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