Remote sensing image fusion algorithm based on mutual-structure for joint filtering using saliency detection

Abstract

Multimodality image fusion provides more comprehensive information and has an increasingly wide range of uses. For the remote sensing image fusion, traditional multiresolution analysis (MRA)-based methods always have insufficiencies in contrast with spatial details. At the same time, traditional sum of modified Laplace may do blocking artifacts. In order to overcome these deficiencies, we propose a remote sensing image fusion method based on the mutual-structure for joint filtering and saliency detection. Our method uses joint filtering to facilitate the correct extraction of the high and low frequency from source images. The saliency detection method also improves the effect of low-frequency fusion, and the high-frequency sub-bands calculate the extended sum of modified Laplace for better fusion. The method is compared with other five classical fusion methods. The experimental results show that the algorithm effectively preserves the structural information and textural information of the image and improves the sharpness of the fused image. It turns out to have many advantages in subjective and objective evaluation.