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A novel image fusion algorithm based on nonsubsampled shearlet transform and morphological component analysis

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Abstract

We propose a novel image fusion algorithm which involves nonsubsampled shearlet transform (NSST) and morphological component analysis (MCA). The source images are decomposed into several subbands of different scales and directions by NSST. MCA is performed on the low-pass subbands to extract more salient features, and then, the separated cartoon parts and texture parts are fused, respectively. The larger high-pass subbands coefficients are selected by sum-modified-Laplacian scheme in order to obtain more useful information from the source images. The final fused image can be reconstructed by performing inverse NSST on the fused subbands. Experiments on different kinds of images verify the effectiveness of the proposed algorithm, and experimental results show that the proposed algorithm outperforms other methods in both the visual effect and objective evaluation.

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Xingbin Liu, Wenbo Mei, Huiqian Du & Jiadi Bei

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  1. Xingbin Liu
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  2. Wenbo Mei
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  3. Huiqian Du
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  4. Jiadi Bei
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Correspondence to Huiqian Du.

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Liu, X., Mei, W., Du, H. et al. A novel image fusion algorithm based on nonsubsampled shearlet transform and morphological component analysis. SIViP 10, 959–966 (2016). https://doi.org/10.1007/s11760-015-0846-5

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  • DOI: https://doi.org/10.1007/s11760-015-0846-5

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