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Proceedings Paper

A novel adaptive multi-focus image fusion algorithm based on PCNN and sharpness
Author(s): Qiguang Miao; Baoshu Wang
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Paper Abstract

A novel adaptive multi-focus image fusion algorithm is given in this paper, which is based on the improved pulse coupled neural network(PCNN) model, the fundamental characteristics of the multi-focus image and the properties of visual imaging. Compared with the traditional algorithm where the linking strength, βij, of each neuron in the PCNN model is the same and its value is chosen through experimentation, this algorithm uses the clarity of each pixel of the image as its value, so that the linking strength of each pixel can be chosen adaptively. A fused image is produced by processing through the compare-select operator the objects of each firing mapping image taking part in image fusion, deciding in which image the clear parts is and choosing the clear parts in the image fusion process. By this algorithm, other parameters, for example, Δ, the threshold adjusting constant, only have a slight effect on the new fused image. It therefore overcomes the difficulty in adjusting parameters in the PCNN. Experiments show that the proposed algorithm works better in preserving the edge and texture information than the wavelet transform method and the Laplacian pyramid method do in multi-focus image fusion.

Paper Details

Date Published: 20 May 2005
PDF: 9 pages
Proc. SPIE 5778, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV, (20 May 2005); doi: 10.1117/12.603092
Show Author Affiliations
Qiguang Miao, Xidian Univ. (China)
Guilin Univ. of Electronic Technology (China)
Baoshu Wang, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 5778:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV
Edward M. Carapezza, Editor(s)

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