Multi-Modal Image Fusion via Sparse Representation and Multi-Scale Anisotropic Guided Measure

Abstract

The multi-modal image fusion plays an important role in various fields. In this paper, a novel multi-modal image fusion method based on robust principal component analysis (RPCA) is proposed, which consists of low-rank components fusion and sparse components fusion. In the low-rank components fusion part, a universal low-rank dictionary is constructed for sparse representation (SR) and the low-rank fusion is converted to sparse coefficients fusion by adopting the batch-OMP. In the sparse components fusion part, the anisotropic weight map is constructed to express salient structures of the images. Moreover, a multi-scale anisotropic guided measure is proposed to guide the fusion process, which can extract and preserve the scale-aware salient details of sparse components. Finally, the multi-modal fusion can be achieved by combining two fusion parts together. The experimental results validate that the proposed method outperforms nine state-of-the-art methods in multi-modal fusion both at gray-gray and gray-color scales, in terms of qualitative and quantitative evaluations.