Abstract
In this paper, a remote sensing image fusion method is presented since sparse representation (SR) has been widely used in image processing, especially for image fusion. Firstly, we used source images to learn the adaptive dictionary, and sparse coefficients were obtained by sparsely coding the source images with the adaptive dictionary. Then, with the help of improved hyperbolic tangent function (tanh) and l 0 − max , we fused these sparse coefficients together. The initial fused image can be obtained by the image fusion method based on SR. To take full advantage of the spatial information of the source images, the fused image based on the spatial domain (SF) was obtained at the same time. Lastly, the final fused image could be reconstructed by guided filtering of the fused image based on SR and SF. Experimental results show that the proposed method outperforms some state-of-the-art methods on visual and quantitative evaluations.
Highlights
By making full use of the complementary information of the remote sensing images and other source images of the same scene, image fusion can be defined as the processing method for integrating this information together to obtain a fused image, which is more suitable for the human visual system [1]
Most researchers conduct image fusion based on pixel-fusion [6,7], such as the image fusion method based on the spatial domain, and the image fusion method based on the transform domain
Due to the good performance of sparse representation and the rich information in the spatial domain, this paper presents one new remote sensing image fusion method based on sparse representation and guided filtering
Summary
By making full use of the complementary information of the remote sensing images and other source images of the same scene, image fusion can be defined as the processing method for integrating this information together to obtain a fused image, which is more suitable for the human visual system [1]. The rapidly developing sparse representation methods can more sparsely represent the source images, and effectively extract the potential information hidden in the source images and produce more accurate fused images, compared with the multi-scale transforms [12,13,14]. Based on these findings, scholars apply sparse representation to image fusion. (3) The image fusion methods based on SR can obtain the fused image by sparsely coding the source images and fusing the sparse coefficients It ignores the correlation of the image information in the spatial domain and loses some important detailed information of the source images.
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