Multi-sensor image fusion based on fourth order partial differential equations

TLDR: A new image fusion algorithm based on fourth order partial differential equations and principal component analysis and its performance is superior to state-of-the-art fusion methods, which makes it suitable for real time applications.

Abstract: In this paper, a new image fusion algorithm based on fourth order partial differential equations and principal component analysis is introduced. This is for the first time fourth order partial differential equations brought into the context of image fusion. The proposed algorithm is as follows: First, fourth order partial differential equations are applied on each source image to obtain approximation and detail images. Second, principal component analysis is applied on detail images to obtain optimal weights. Third, final detail image is obtained by fusing these detail images with help of optimal weights. Fourth, final approximation image is obtained by employing an average operation on approximation images. Finally, resultant fused image is calculated by combining the final approximation and detail images. Experiments are conducted on standard fusion datasets. Results are analyzed with help of petrovic metrics and further compared with traditional and recent fusion methods. Results justify that performance of the proposed method is superior to state-of-the-art fusion methods. Moreover, reasonable computational time, easy and effective implementation of the proposed method makes it suitable for real time applications.