Multi-focus image fusion based on HOSVD and parameter adaptive PCNN in fast local Laplacian filtering domain

Abstract

To augment edge preservation capabilities and detail enhancement in multi-focus image fusion, a new Multi-focus image fusion algorithm is proposed in this paper. Firstly, we select fast local laplacian filtering (FLLF) as the multi-scale decomposition tool to decompose the source image into an approximate image and multiple detail images. Secondly, higher-order singular value decomposition (HOSVD) is applied to fuse the approximate image. Thirdly, the parameter-adaptive pulse coupled-neural network (PA-PCNN) model is used to fuse the detailed images. Finally, exploiting the combination of the fusion results of the approximate image and the detail images, the final fusion image is obtained using the FLLF inverse transform. The experimental results show that the proposed method achieves excellent performance in both subjective visual perception and objective index evaluation.