Multi Sensor Image Fusion using Empirical Mode Decomposition

Image fusion is a process of combining relevant information from two or more images from different sensors based on certain algorithm. Many algorithms have been proposed for pixel level image fusion. In this paper, Empirical Mode Decomposition is the recent, powerful tool for adaptive multi scale analysis of non stationary signals that decomposes them into Intrinsic Mode Functions (IMFs). Hence an attempt is made to use EMD for multi sensor image fusion. Two types of Empirical Mode Decomposition algorithms viz. BEMD (Bi dimensional Empirical Mode Decomposition) and VEMD (Vectorized Empirical Mode Decomposition) are used to decompose the images to get Intrinsic Mode Functions (IMFs). It is concluded that both algorithms are performed similar but VEMD is computationally very simple. Fusion algorithms viz., Simple Averaging (SA), Principal Component Analysis (PCA), Stationary Wavelet Transform (SWT) and Laplacian Pyramid (LP) are applied on each IMFs to generate the fused IMFs. Fused image is reconstructed by summing all the fused IMFs. Objective and subjective fusion quality evaluation metrics are used to evaluate the performance of these fusion algorithms. It is concluded that SWT based image fusion algorithm performs better followed by LP based fusion algorithm. It is also concluded that fusion quality is degraded by using more number of decomposition levels in wavelets and pyramid based image fusion algorithms. From this study, it is concluded that both BEMD and VEMD with SWT based image fusion algorithm provides good fusion results. VEMD with SWT based image fusion algorithm is computationally simple and can be used for real time image fusion applications.