Abstract
Considering the problems of the limited energy in wireless multi-media sensor networks (WMSNs) and the focused regions discontinuity of the fused image obtained using traditional multi-scale analysis tools (MST)-based methods, an effective multi-focus image fusion algorithm is proposed in this paper. In this method, the original fused image is obtained based on wavelet transform where the low-frequency coefficients are fused by average scheme, whereas the high-frequency coefficients are fused by the proposed merging rule consisting of the grey relation analysis of similarity and local area energy. Then, grey absolute relation analysis is again utilized as measurement indicator to estimate the similarities between the initial fused image and source images, during which the initial map is acquired and then corrected by the mathematical morphological opening and closing. Finally, the fused image is obtained with the guidance of the corrected map, namely the decision map. Experiment results demonstrate that the fused image using the proposed algorithm is more continuous in focused region and more similar to source images in brightness compared with state-of-art multi-focus image fusion algorithms, such as Curvelet transform, lifting stationary wavelet transform (LSWT), non-subsampled contourlet transform (NSCT) and non-subsampled shearlet transform (NSST). Meanwhile, the proposed method shows better superiority in term of the computational complexity.
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Acknowledgements
The authors thank the editors and the anonymous reviewers for their detailed review, valuable comments and constructive suggestions. This work is supported by the National Natural Science Foundation of China (No. 61733004 and 61573134), National Science and Technology Support Program of the Ministry of Science and Technology of China (No. 2015BAF13B00), Natural Science Foundation of Human Province of China (No. 2018JJ3079).
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Liu, C., Long, Y., Mao, J. et al. An effective image fusion algorithm based on grey relation of similarity and morphology. J Ambient Intell Human Comput 14, 14859–14872 (2023). https://doi.org/10.1007/s12652-018-0873-5
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DOI: https://doi.org/10.1007/s12652-018-0873-5