Study on ellipse directional window and new contourlet in multifocus image fusion algorithm

Abstract: For the multifocus image fusion problem in extending depth of focus of optical microscope, this paper proposes a novel fusion method of multifocus images, which is based on directional statistics in new contourlet domain. Firstly, in order to obtain low-frequency and high-frequency coefficients in subbands with different scales and directions, the algorithm decomposed the multifocus images by using the new contourlet transform. Secondly, the low-frequency coefficients were fused by arithmetic mean. High-frequency coefficients fusion was divided into two steps. The first step was extracting the characteristic value by using Sum Modified Laplacian(SML), and then a new ellipse directional window was defined, which is corresponding to directional decomposition. Based on the determined parameters of directional window, accumulating the extracted characteristic value and using it as the basis for the high-frequency coefficients fusion. Finally, the fused image was obtained through inverse new contourlet transform. In the experimental section, fusion algorithms were evaluated by the new objective evaluation metric, Mutual Structural Information(MSI), which considers both statistical property between the fused image and source images and human visual characteristics. Experimental results show that for multifocus image, compared to square window in the domain of the new contourlet transform, square window and ellipse directional window in the domain of the contourlet transform, mutual structural information of the proposed method were improved by 2.94%, 10.44% and 8.56%, respectively. The work illustrates that the proposed method can extract more sharpness information of source images to fusion image. It is an effective means of extended depth of focus.