Algebraic Multi-Grid Based Multi-Focus Image Fusion Using Watershed Algorithm

This paper proposes a new multi-focus image fusion method named AMGW, and it is based on algebraic multi-grid (AMG) algorithm and watershed segmentation method. In the implementation, the coarse grids of the source images are first extracted with the affinity matrix, and with a spatial interpolation function the approximation of the source image can be reconstructed from the coarse grids. A considerable amount of edge and textural information is still preserved in such approximation. The two source images are compared with their corresponding approximation block by block respectively by employing the mean square error (MSE) as a sharpness criterion. The MSE values are then used to identify the blocks of higher fidelity from the source images. The watershed segmentation is applied to those uncertain blocks in one source image. The two source images are compared again with the MSEs of the segmented regions. The fused image is obtained by reserving the blocks and regions with higher MSEs and applying a post-processing operation. Experimental results demonstrate that the AMG-based method outperforms the state-of-the-art fusion approaches in terms of selected objective image quality assessments. The details of the source images are well preserved in the fused image.