Medical image fusion based on NSCT and sparse representation

Abstract

Image fusion is to get a fused image that contains all important information from source images of the same scene. Meanwhile, multi-scale transforms and sparse representation (SR) are the two most effective techniques for image fusion. However, the SR-based image fusion methods are time-consuming and do not take the structural information of the source images into consideration. In addition, different multi-scale transform-based methods have their inevitable defects waiting to be solved till now. Therefore, in this paper, a new image fusion method combining nonsubsampled contourlet transform (NSCT) with SR is proposed. A decision map for the low-frequency coefficients according to the high-frequency coefficients is made to overcome these problems. Furthermore, it can reduce the calculation cost of the fusion algorithm and retain the useful information of source images as far as possible. Comparing with conventional multi-scale transform based methods and sparse representation based methods with a fixed or learned dictionary, the proposed method has better fusion performance in the field of medical image fusion.