Multifocus image fusion based on coefficient significance of redundant discrete wavelet transform

Abstract

Purpose

This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision map is very important the fusion results. In the processing of distinguishing the well-focus part with blur part in an image, the edge between the parts is more difficult to be processed. Coefficient significance is very effective in generating the better decision map to fuse the multifocus images.

Design/methodology/approach

The energy of Laplacian is used in the approximation coefficients of redundant discrete wavelet transform. On the other side, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient.

Findings

Due to the shift-variance of the redundant discrete wavelet and the effectiveness of fusion rule, the presented fusion method is superior to the region energy in harmonic cosine wavelet domain, pixel significance with the cross bilateral filter and multiscale geometry analysis method of Ripplet transform.

Originality/value

In redundant discrete wavelet domain, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient of source images.

Citation

Liu, J., Geng, P. and Ma, H. (2019), "Multifocus image fusion based on coefficient significance of redundant discrete wavelet transform", Industrial Robot, Vol. 46 No. 3, pp. 377-383. https://doi.org/10.1108/IR-11-2018-0229