Two‐stage progressive residual learning network for multi‐focus image fusion

Abstract

Boundary artifacts and color detail distortion are easily caused by the common multi‐focus image fusion methods. In order to solve this problem, we propose a two‐stage progressive residual learning network for multi‐focus image fusion. The proposed network can progressively learn color information and detail features through end‐to‐end mapping. The whole network is composed of two sub‐networks: the initial fusion block network and the enhanced fusion block network. First, the color information in the source image is fused by the initial fusion block network to generate the initial fusion image. Then on the basis of the initial fusion image, the detailed features of the source image are further fused by enhanced the fusion network to form the final fusion image. In order to solve the problem of lack of groundtruth when multi‐focus image fusion is carried out with supervised method, the multi‐focus image fusion problem is compared to the easy‐to‐solve image restoration problem. A synthetic dataset for network training is generated by ”degenerating” the VOC2012 dataset according to the set rules. After training, the method works well for fusion tasks without further processing. Experimental results show that the proposed method is superior to the existing methods in subjective visual perception and objective quantitative evaluation.