10 April 2018 Multi-focus image fusion based on area-based standard deviation in dual tree contourlet transform domain
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061533 (2018) https://doi.org/10.1117/12.2305478
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Multiresolution-based methods, such as wavelet and Contourlet are usually used to image fusion. This work presents a new image fusion frame-work by utilizing area-based standard deviation in dual tree Contourlet trans-form domain. Firstly, the pre-registered source images are decomposed with dual tree Contourlet transform; low-pass and high-pass coefficients are obtained. Then, the low-pass bands are fused with weighted average based on area standard deviation rather than the simple “averaging” rule. While the high-pass bands are merged with the “max-absolute’ fusion rule. Finally, the modified low-pass and high-pass coefficients are used to reconstruct the final fused image. The major advantage of the proposed fusion method over conventional fusion is the approximately shift invariance and multidirectional selectivity of dual tree Contourlet transform. The proposed method is compared with wavelet- , Contourletbased methods and other the state-of-the art methods on common used multi focus images. Experiments demonstrate that the proposed fusion framework is feasible and effective, and it performs better in both subjective and objective evaluation.
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Min Dong, Min Dong, Chenghui Dong, Chenghui Dong, Miao Guo, Miao Guo, Zhe Wang, Zhe Wang, Xiaomin Mu, Xiaomin Mu, } "Multi-focus image fusion based on area-based standard deviation in dual tree contourlet transform domain", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061533 (10 April 2018); doi: 10.1117/12.2305478; https://doi.org/10.1117/12.2305478
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