A generative adversarial network with adaptive constraints for multi-focus image fusion

In this paper, we propose a novel end-to-end model for multi-focus image fusion based on generative adversarial networks, termed as ACGAN. In our model, due to the different gradient distribution between the corresponding pixels of two source images, an adaptive weight block is proposed in our model to determine whether source pixels are focused or not based on the gradient. Under this guidance, we design a special loss function for forcing the fused image to have the same distribution as the focused regions in source images. In addition, a generator and a discriminator are trained to form a stable adversarial relationship. The generator is trained to generate a real-like fused image, which is expected to fool the discriminator. Correspondingly, the discriminator is trained to distinguish the generated fused image from the ground truth. Finally, the fused image is very close to ground truth in probability distribution. Qualitative and quantitative experiments are conducted on publicly available datasets, and the results demonstrate the superiority of our ACGAN over the state-of-the-art, in terms of both visual effect and objective evaluation metrics.