Performance improvement scheme of multifocus image fusion derived by difference images

Abstract

In multiscale transform (MST)-based multifocus image fusion, the fusion rules of different subbands are a significant factor that affects the fusion performance. However, dependence only on new fusion rule will see no significant performance gain for a MST-based method. To address this problem, this paper proposes two novel multifocus image fusion techniques based on multi-scale and multi-direction neighbor distance (MMND), in which the improvements of the fusion performance are respectively achieved by two new developed updating schemes. These two schemes are constructed according to the fact that the difference between a low quality fused result and the source image in the focused region is sharper than those generated by a high quality fused result. Based on this fact, the pixels of the source images are classified into three types in the updating mechanism, namely, pixels of focused significant regions, pixels of smooth regions, pixels of transition area between the focused and defocused regions. According to the categories of source images pixels, we can update the fused result produced by the MMND method in spatial and the MMND domain. Extensive experimental results validate that the proposed two fusion schemes can achieve better results than some state-of-the art algorithms.