Multiscale channel attention network for infrared and visible image fusion

Imaging systems with different imaging sensors are widely applied to surveillance field, military field, and medicine field. Particularly, infrared imaging sensors can acquire thermal radiations emitted by different objects but lack textural details, and visible imaging sensors can capture abundant textural information but suffer from loss of scene information under poor weather conditions. The fusion of infrared and visible images can synthesize a new image with complementary information of the source images. In this paper, we present a deep learning method with encoder-decoder architecture for infrared and visible image fusion. Firstly, multiscale channel attention blocks are introduced to extract features at different scales, which can preserve more meaningful information and enhance the important information. Secondly, we utilize the improved fusion strategy based on visual saliency to fuse feature maps. Lastly, the fusion result is restored via reconstruction network. In comparison with other state-of-the-art approaches, our experimental results achieve appealing performance on visual effects and objective assessments.