Abstract
Image fusion techniques are applied to the synthesis of two or more images captured in the same scene to obtain a high-quality image. However, most of the existing fusion algorithms are aimed at single-mode images. To improve the fusion quality of multi-modal images, a novel multi-sensor image fusion framework based on non-subsampled shearlet transform (NSST) is proposed. First, the proposed solution uses NSST to decompose source images into high- and low-frequency components. Then, an improved pulse coupled neural network (PCNN) is proposed to process high-frequency components. Thus, the feature extraction effect of the high-frequency component is meliorated. After that, a sparse representation (SR) based measure, including compact dictionary learning and Max-L1 fusion rule, is designed to enhance the detailed features of the low-frequency component. Finally, the final image is obtained by the reconstruction of high- and low-frequency components via NSST inverse transformation. The proposed method is compared with several existing fusion methods. The experiment results show that the proposed algorithm outperforms other algorithms in both subjective and objective evaluation.
Highlights
Image fusion techniques [36], [38] process multiple images from the same scene, extract useful information, and integrate them into a high-quality image
In order to improve the preservation of details and energy, this paper proposes a new image fusion framework for nonsubsampled shearlet transform (NSST) based on the transform domain image fusion algorithm
The fused image is obtained by inverse transform of non-subsampled shearlet transform (NSST)
Summary
Image fusion techniques [36], [38] process multiple images from the same scene, extract useful information, and integrate them into a high-quality image. The integrated image is more informative than source images. The corresponding visual effects are improved for the observation in human visual system. Image fusion is commonly used in different areas, such as multi-focus image fusion [25], [49], infrared-visual image fusion [39], and medical image fusion [29], [42]. Image fusion has gradually become a hot research topic, and various image fusion algorithms have been proposed. These algorithms can be roughly divided into two categories: spatial and transform domain based algorithms [46]
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