A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation

Abstract

In this paper, a new Multi-Focus Image Fusion (MFIF) method based on multi-source joint layering and Convolutional Sparse Representation (CSR) is proposed. Based on the characteristics of multi-focus source images, a multi-source joint layering regularization model was designed to divide the sources into a common base-layer and respective focus detail-layers. This strategy can overcome the defects caused by source layering separately effectively. In detail-layer fusion, CSR was employed to extract and global features. It can avoid detail blur and high computational cost caused by image blocking in the conventional sparse representation model. The proposed detail-layer fusion rule combined the CSR coefficient maps pairwise with the window based select-max rule. In the experiments, the optimal layering parameter was selected by experiments at first, and then five recently proposed specific MFIF or general image fusion algorithms were contrasted with the proposed method by plenty of subjective and objective experimental comparisons. The experimental results demonstrated the superiority of the authors’ method.