Multi-focus Image Fusion Algorithms Research Based on Curvelet Transform

Due to the limited depth-of-focus of optical lenses, it is often difficult to get an image that contains all relevant objects in focus. Multi-focus image fusion algorithms can solve this problem effectively. Based on the analysis of the most widely used multi-focus image fusion algorithms, a new curvelet transform based multi-focus image fusion algorithm is proposed in this paper. According to the different frequency areas decomposed by Curvelet transform, the selection rules of the low frequency coefficients and the high frequency coefficients are discussed respectively. Low frequency coefficients and the high frequency coefficients are fused with NGMS (Neighborhood Gradient Maximum Selectivity) and LREMS (Local Region Energy Maximum Selectivity) separately in this paper. It is shown that the multi-focus image fusions by the proposed algorithm can obtain the same scene in focus image fusion, and have protruding advantage over other algorithms on subjective evaluation and objective evaluation.