DDF-GAN: A Generative Adversarial Network with Dual-Discriminator for Multi-Focus Image Fusion

Shiyu Chen, Xin Jin, Qian Jiang, Jie Yang, Ting Cao, Xiuliang Xi, Yunyun Dong

Abstract: Multi-focus image fusion can overcome the issues that optical lens imaging cannot focus multiple targets simultaneously due to the depth of field limitation. In this paper, we propose a generative adversarial network (DDF-GAN) which consists of a generator and two discriminators to directly generate fused images without decision maps and post-processing. In training process, the source all-in-focus image and the fused image generated by generator are used as input to one of the dual discriminators. Meanwhile, the gradient map of the fused image and the source gradient map of the source all-in-focus image are used as input to another discriminator. An adversarial relationship is established to enhance texture details of fused image. In addition, we create a data set and use it as the main training set for the proposed model. Abundant experiments were carried out to verify the availability of our method. Experimental results prove that our method has advantages in subjective visual perception of human and quantitative measurement.