DCTDFusion: a dual-branch network based on dilated convolution and transformer for infrared and visible image fusion

Fusion of texture information and semantic information from visible and infrared images is important for many fields. However, existing algorithms extract features with a limited range of receptive fields which causes the loss of contextual information. This paper introduces DCTDFusion, a two-branch network integrating dilated convolution and Transformer to address this issue. We propose Residual Dilated Convolutions Block (RDCB) to extract local information of different receptive field ranges. To get the global information at multiple scales, we introduce the Transformer Dilated Convolutions Block. In addition, the Sobel feature extractor is used to retain the gradient information of both branches. Numerous experimental evidences show that the fused images of DCDTFusion contain rich information of significant targets and background in source images. Additionally, our method improves about 20%, 4%, 1% and 2% in MI, VIF, QAB/F and SSIM metrics comparing with the second best.