  Deep learning is a rapidly developing approach in the field of infrared and
visible image fusion. In this context, the use of dense blocks in deep networks
significantly improves the utilization of shallow information, and the
combination of the Generative Adversarial Network (GAN) also improves the
fusion performance of two source images. We propose a new method based on dense
blocks and GANs , and we directly insert the input image-visible light image in
each layer of the entire network. We use SSIM and gradient loss functions that
are more consistent with perception instead of mean square error loss. After
the adversarial training between the generator and the discriminator, we show
that a trained end-to-end fusion network -- the generator network -- is finally
obtained. Our experiments show that the fused images obtained by our approach
achieve good score based on multiple evaluation indicators. Further, our fused
images have better visual effects in multiple sets of contrasts, which are more
satisfying to human visual perception.
