Multi-focus image fusion based on SIFT dictionary learning and guided filtering

Abstract:

In order to solve the problem that the local detail retention ability, spatial continuity and non-registration problems of most multi-focus image fusion algorithms cannot be improved at the same time, this paper proposes a multi-focus image fusion algorithm based on SIFT dictionary learning and guided filtering. The algorithm overcomes the problem that the low rank representation of image can capture the global structure but could not preserve the local structure by learning sub-dictionaries. The classification of the sub-dictionaries utilizes the translation invariance and the scale invariance etc. of SIFT to eliminate the fusion artifacts of unregistered images. In addition, the adaptive-window guided filtering is performed during the low rank representation coefficients fusion progress, which increases the spatial continuity of fused image. Pixels with rich texture assign to small window, while weak texture pixels choose large window. We select 6 groups of data, including 3 groups of widely used images and 3 groups of real-world images for verifying the validity of the proposed algorithm. Experimental results show that this algorithm outperforms the current mainstream multi-focus image fusion algorithms from qualitative analysis and quantitative analysis.