Shearlet transform based technique for image fusion using median fusion rule

Image fusion is a challenging research area which is useful in various image processing applications. Image fusion integrates information from multiple source images into a single composite image for better visual quality and information content than any of its source images. In the present paper, we have proposed a new median based image fusion algorithm using nonsubsampled shearlet transform. Nonsubsampled shearlet transform is a powerful multiscale geometrical analysis (MGA) tool having rich mathematical structure, high directionality, anisotropy and shift-invariance features. Due to these features nonsubsampled shearlet transform can efficiently capture information of the source images in its coefficient sets. The coefficient sets of the source images are fused by using a new median based fusion rule. Median is an important statistical measurement, which is enriched with two outstanding properties that are edge preserving and robustness against noise. Hence, median based fusion rule increases the quality of fused image. The proposed fusion rule is simple and easy to understand. Strength of the proposed fusion method is verified visually as well as quantitatively by comparing it with different state of the art methods. We have performed experiments on three different types of images (medical, remote sensing and multifocus). Results of the experiments confirm that the proposed method outperform in comparison with other state-of-the-art fusion methods visually as well as quantitatively in terms of different quantitative performance measures such as entropy, standard deviation, edge strength, fusion factor, and running time.