In this paper, a novel multi-focus image fusion approach is presented. Firstly, a joint dictionary is constructed by combining several sub-dictionaries which are adaptively learned from source images using K-singular value decomposition (K-SVD) algorithm. The proposed dictionary constructing method does not need any prior knowledge, and no external pre-collected training image data is required either. Secondly, sparse coefficients are estimated by the batch orthogonal matching pursuit (batch-OMP) algorithm. It can effectively accelerate the sparse coding process. Finally, a maximum weighted multi-norm fusion rule is adopted to accurately reconstruct fused image from sparse coefficients and the joint dictionary. It can enable the fused image to contain most important information of the source images. To comprehensively evaluate the performance of the proposed method, comparison experiments are conducted on several multi-focus images and manually blurred images. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques, in terms of visual and quantitative evaluations.
['Hongpeng Yin', 'Yanxia Li', 'Yi Chai', 'Zhaodong Liu', 'Zhiqin Zhu']
A novel sparse-representation-based multi-focus image fusion approach