CFFusion: Cross Framework for Multi-focus Image Fusion

Abstract

As a powerful assistance technique for computer vision, multi-focus images fusion has attracted extensive attention in recent years. However, how to accurately detect the focused region from the focused source image is a key problem. To address the weak performance of current fusion methods, in this paper, we proposed a new multi-focus images fusion method. Firstly, the encoder based on VGG-16 aims to extract multi-scale features of the source images. Then, the cross features attention fusion module (CFAFM) integrates multi-scale features for subsequent decoding. Further, the refinement module (RM) aims to refine the predicted coarse decision map. Finally, an all-in focus image is obtained by using the decision map and source images according to the designed fusion rules. Extensive experiments on ‘Lytro’ dataset demonstrate that the proposed method significantly outperforms 5 typical image fusion models and achieves the new state-of-the-art.