Abstract
In this paper, a multi-focus image fusion method based on non-subsampled shearlet transform (NSST) and backpropagation (BP) neural network is proposed. Firstly, the NSST is performed on each source image and the NSST contrast of the image is calculated according to the high-frequency and low-frequency coefficients. Then the NSST contrast of the partial region of the source image is selected as the training sample for the feedforward neural network which is updated by the back propagation method. Thirdly, the focus and de-focus area is classified by the trained neural network, and the focus area is fused. Then consistency check is performed on the specific pixels determined by the neural network. The simulation results show that the image fusion effect of this method has better advantages in both subjective and objective evaluation.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61763011) and the Science and Technology Program of Educational Department of Jiangxi province (GJJ150526).
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Huang, X., Zi, R., Li, F. (2020). Multi-focus Image Fusion Method Based on NSST and BP Neural Network. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M. (eds) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Smart Innovation, Systems and Technologies, vol 179. Springer, Singapore. https://doi.org/10.1007/978-981-15-3863-6_11
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DOI: https://doi.org/10.1007/978-981-15-3863-6_11
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