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[2021-Vol.18-Issue 6]An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion
Post: 2021-12-23 14:13  View:404

Journal of Bionic Engineering (2021) 18:1452–1462 https://doi.org/10.1007/s42235-021-00100-0

An Improved Pigeon?Inspired Optimization for Multi-focus Noisy Image Fusion

Yingda Lyu1,4 · Yunqi Zhang2  · Haipeng Chen3,4
1 Public Computer Education and Research Center, Jilin University, Changchun 130012, China
2 College of Software, Jilin University, Changchun 130012, China
3 College of Computer Science and Technology, Jilin University, Changchun 130012, China
4 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

Abstract  Image fusion technology is the basis of computer vision task, but information is easily afected by noise during transmission. In this paper, an Improved Pigeon-Inspired Optimization (IPIO) is proposed, and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation. By two-scale image decomposition, the input image is decomposed into base layer and detail layer. For the base layer, IPIO algorithm is used to obtain the optimized weights for fusion, whose value range is gained by fusing the edge information. Besides, the global information entropy is used as the ftness index of the IPIO, which has high efciency especially for discrete optimization problems. For the detail layer, the fusion of its coefcients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain. The sum of the above base and detail layers is as the fnal fused image. Experimental results show that the proposed algorithm has a better fusion efect compared with the recent algorithms. 

Keywords  Improved pigeon-inspired optimization · Convolutional sparse representation · Noisy image fusion · Bionic algorithm

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A portion of multi-focus images used in our experiments

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