MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
This work addresses multi-focus image fusion for computer vision applications, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled the problem of defocus spread effect in multi-focus image fusion by proposing MFIF-GAN, which generates focus maps to attenuate this effect and outperforms state-of-the-art methods in visual perception, quantitative analysis, and efficiency.
Multi-Focus Image Fusion (MFIF) is a promising image enhancement technique to obtain all-in-focus images meeting visual needs and it is a precondition of other computer vision tasks. One of the research trends of MFIF is to avoid the defocus spread effect (DSE) around the focus/defocus boundary (FDB). In this paper,we propose a network termed MFIF-GAN to attenuate the DSE by generating focus maps in which the foreground region are correctly larger than the corresponding objects. The Squeeze and Excitation Residual module is employed in the network. By combining the prior knowledge of training condition, this network is trained on a synthetic dataset based on an α-matte model. In addition, the reconstruction and gradient regularization terms are combined in the loss functions to enhance the boundary details and improve the quality of fused images. Extensive experiments demonstrate that the MFIF-GAN outperforms several state-of-the-art (SOTA) methods in visual perception, quantitative analysis as well as efficiency. Moreover, the edge diffusion and contraction module is firstly proposed to verify that focus maps generated by our method are accurate at the pixel level.