Adaptive Region-Segmentation Multi-Focus Image Fusion Based on Differential Evolution

Abstract

An adaptive region-segmentation based multi-focus image fusion method is presented using a Laplacian pyramid transform which decomposes the pre-registered source images into approximate and detail coefficients. In order to avoid the disadvantage of fixed-size blocks, the adaptive differential evolution scheme is designed to compute the optimal-size block. Firstly, with approximate coefficients, the optimal-size blocks are iteratively calculated by an adaptive differential evolution algorithm. The initial decision diagram is then completed by comparing the regional sum-modified Laplacian energy of two corresponding blocks after the regional sum-modified Laplacian energy is calculated. Secondly, the initial decision diagram is optimized by the guided image filter to obtain the final decision diagram in order to avoid the block effect of boundary. With the decision diagram, the approximate coefficients are fused using the weighted mean rules, while the detail coefficients are fused using the regional gradient energy method. Finally, an inverse Laplacian pyramid transform is used to reconstruct the fused approximate coefficients and fused detail coefficients, and to acquire the fused image where all objects are clear. The experimental result proves that the proposed method produces fusion images of fewer artifacts or additional noise, with higher computational efficiency. The proposed method is also superior to the other state-of-the-art methods in both subjective visual effect and objective quantitative evaluation indicators.