Multi-Focus Image Fusion using Self-Similarity and Depth Information in Nonsubsampled Shearlet Transform Domain

Abstract

Combined with the shared similarity among multiple source images and depth of field in a camera, a new image fusion algorithm based on nonsubsampled shearlet transform (NSST) domain is proposed. First, NSST is utilized for decomposition of the source images, the low frequency coefficients is fused by weight votes in the structure-driven regions by using shared similarity and depth of field (SSSID), and then apply larger sum-modified-Laplacian (SML) with depth of field to the high frequency coefficients, finally the fusion image is gained after we do the inverse NSST to the fused coefficients. The algorithm can not only preserve the information of the source images well, but also suppress pixel distortion due to nonlinear operations in transform domain. Experimental results demonstrate that the proposed method outperforms the state-of-the-art transform domain fusion methods on image quality and objective fusion criteria.