Infrared and visible image fusion algorithm based on a cross-layer densely connected convolutional network

To preserve the saliency of targets in infrared images and the textures in visible images, a novel infrared and visible image fusion method, to the best of our knowledge, is proposed. First, we design a densely connected convolutional network that contains an encoder, fusion, and decoder to minimize the omission of source image effective information. Then, a loss function based on the variational model is designed to retain the thermal radiation information of the infrared image and the details of the visible image to the greatest extent. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of six metrics and better preserves the clear target and textures of infrared and visible images.