Infrared and visible light image fusion is an important issue in the field of infrared technology. A variational model based on saliency preservation is proposed to help fusion process for infrared and visible light image by enhancing the saliency information of source images. The proposed variational model is composed by the weighted fusion term and the gradient fusion term. A weight function constructed by the saliency map of the source images is proposed to form the weighted fusion term. While in the gradient fusion, the target gradient is obtained by the maximum eigen-value and corresponding eigen-vector of the weighted structure tensor that also contains the salient information of the source images. Experimental results indicate that the proposed variational model better emphasizes the target information of the source images and outperforms other state-of-the-art fusion methods in both aspects of subjective visual and objective performance.
Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR images. We extract four features of the SAR image, which include the intensity, orientation, uniqueness, and global contrast, as the input of the MSD model. The saliency map is generated by the multitask sparsity pursuit, which integrates the multiple features collaboratively. Detection of different scale features is also taken into consideration. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps obtained by the MSD model, we apply the saliency map of the SAR image to the SAR and color optical image fusion. The experimental results of real data show that the saliency map obtained by the MSD model helps to improve the fusion effect, and the salient areas in the SAR image can be highlighted in the fusion results.
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