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.
Highlights
With the development of the modern technology, the requirement for the completeness of the information acquisition is increasing, so the multi-sensors play an important role in many fields
Current image fusion methods are mainly divided into spatial domain based and transform domain based according to their processing domain [6], [7]
The proposed method is compared with nine image fusion methods, i.e., the morphological difference pyramid (MDP) based method [43], the gradient pyramid (GP) based method [44], the sparse representation (SR) based method [22], the dual-tree complex wavelet transform (DTCWT) based method [19], the nonsubsampled contourlet transform (NSCT) based method [20], the NSCT pulse coupled neural network based method [45], zhang’s method [10], ASR method [46] and GFCE method [47]
Summary
With the development of the modern technology, the requirement for the completeness of the information acquisition is increasing, so the multi-sensors play an important role in many fields. To better obtain the composite image for further visual and processing tasks, the image fusion has become a research hotspot and been widely employed in computer vision, military surveillance, medical imaging, remote sensing, and so on [1]–[5]. Current image fusion methods are mainly divided into spatial domain based and transform domain based according to their processing domain [6], [7]. The fused image can be constructed through the combination of the input images at pixel-level or block-level. These methods mainly select salient pixels or regions with higher clarity to fuse the multi-modal images [8]. The direct fusion in pixellevel will lead to decreasing the edge contrast, and the region
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