A multi-focus image fusion network with local-global joint attention module

Abstract

Multi-focus image fusion can obtain high-quality images by overcoming the limited depth of field of optical lenses. Benefiting from deep learning, we design a local-global joint attention module and propose a novel multi-focus image fusion network. The module essentially is an attention module. Local and global features are extracted respectively through point-wise convolution and spatial pyramid pooling. A joint attention map is produced by reducing the dimension and fusing these two features. The proposed network is mainly composed of a feature fusion module and two weight-shared dense feature extraction modules, each connected to six consecutive attention modules. Such design has two benefits: adequate extraction of initial features and capturing of local and global features. Subjective visual evaluation demonstrates that the proposed network can preserve the authenticity of fusion results. And it also reduces the appearance of artifacts and detail losses between the focus and defocus regions. Objective metric evaluation shows that the proposed network outperforms most of the existing models, such as SwinFusion, GACN, and UFA-FUSE, in Lytro, MFI-WHU, and MFFW datasets. Ablation experiments demonstrate that the design of attention and the overall framework of the network is reasonable. Overall, the proposed model can finish the multi-focus image fusion task with high quality.