Variational multi-source image fusion based on the structure tensor

Abstract

This article describes the variational multi-source image fusion using the structure tensor algorithm, which can keep the image features and details very well. We first narrative the fusion gradient field based on structure tensor, then measure the characteristic graphs of each source image, and thus construct a weight value for the source image gradient according to the characteristic graph. Gradients with high image features are highlighted in the fusion gradient field, and thus image features in the sources are well preserved. By using variational partial differential equation, the fusion image is reconstructed from the target gradient field. From the actual experimental results, the average gradient value and entropy of the fused image are found to be higher than those obtained by using the wavelet transform algorithm, tower decomposition algorithm, and direct gradient fusion algorithm, and the visual effect of the fusion image is good enough to retain the feature of source images and details in it. Therefore, it can give qualified image information for target detection and identification.