基于残差密集块和自编码网络的红外与可见光图像融合

Infrared and visible image fusion is an effective means to obtain high-quality target images in complex environments. It has broad application prospects in the fields of target detection and tracking, image enhancement, remote sensing, and medical treatment. In order to solve the problems of the current deep learning-based infrared and visible image fusion methods that the network cannot fully extract featuresd, cannot fully utilize the feature information, and the clarity of fusion image is low, this paper proposes an end-to-end image fusion network based on residual dense block and auto-encoder, which uses an encoder network based on residual dense block to decompose the image into a background feature map and a detailed feature map, after that the two feature maps will be fused, and then reconstructed by the decoder to restore the final fusion image. The test results show that the method in this paper can obtain a fused image with high definition, prominent target and obvious outline, compared with the current representative fusion methods, the six fusion quality evaluation indicators of SF, AG, CC, SCD, Qabf, and SSIM have been improved in different degrees, especially has a huge advantage in the clarity of the fusion image. And for complex environmental images such as blur, occlusion, backlighting, and smoke, there is a good fusion effect.